package ‘betareg’ was built under R version 4.0.2

In this notebook we conduct exploratory factor analyses (EFAs) on the datasets for our studies of concepts of mental life, in which each participants judged the various mental capacities of a particular target entity. We analyze datasets for adults and children from each of our five field sites: the Ghana, Ghana, Thailand, China, and Vanuatu.

This notebook contains an exploration of how well the cultural model represented by the EFA solution describes the responses of individuals within that culture, and whether this “fit” between individtual and cultural model varies along demographic lines.

NOTE: As of now, the “efa_oblique.Rmd” notebook (or one of the alternative versions of these analyses) must be run prior to this notebook.

Functions

US

US adults

Age


Call:
lm(formula = scale(MSE) ~ scale(age), data = d_sim_us_adults)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.5642 -0.9119 -0.2456  0.9984  1.8021 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0002467  0.0897358  -0.003    0.998
scale(age)   0.1300386  0.0901028   1.443    0.152

Residual standard error: 0.9952 on 121 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.01692,   Adjusted R-squared:  0.008798 
F-statistic: 2.083 on 1 and 121 DF,  p-value: 0.1515

Call:
betareg(formula = MSE_rescaled ~ scale(age), data = d_sim_us_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.7855 -0.8601  0.0118  0.9664  1.4624 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.12880    0.07582 -14.888   <2e-16 ***
scale(age)   0.11029    0.07085   1.557     0.12    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)    6.576      0.807   8.149 3.67e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 67.82 on 3 Df
Pseudo R-squared: 0.02108
Number of iterations: 12 (BFGS) + 2 (Fisher scoring) 

Gender


Call:
lm(formula = scale(MSE) ~ gender, data = d_sim_us_adults)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.5915 -0.8355 -0.1464  0.8871  1.7750 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept) -0.02207    0.08815   -0.25    0.803  
gender_m     0.18686    0.08815    2.12    0.036 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9864 on 125 degrees of freedom
Multiple R-squared:  0.0347,    Adjusted R-squared:  0.02698 
F-statistic: 4.494 on 1 and 125 DF,  p-value: 0.036

Call:
betareg(formula = MSE_rescaled ~ gender, data = d_sim_us_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.7742 -0.8282  0.1007  0.8920  1.4238 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.14404    0.07537 -15.179   <2e-16 ***
gender_m     0.12420    0.07124   1.743   0.0813 .  

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   6.6103     0.7985   8.278   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 70.29 on 3 Df
Pseudo R-squared: 0.02485
Number of iterations: 13 (BFGS) + 2 (Fisher scoring) 

Race/ethnicity


Call:
lm(formula = scale(MSE) ~ ethnicity_cat2, data = d_sim_us_adults)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.4867 -0.9983 -0.1019  0.9747  1.7861 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)
(Intercept)        0.007241   0.092817   0.078    0.938
ethnicity_cat2_POC 0.052715   0.092817   0.568    0.571

Residual standard error: 1.002 on 115 degrees of freedom
  (10 observations deleted due to missingness)
Multiple R-squared:  0.002797,  Adjusted R-squared:  -0.005874 
F-statistic: 0.3226 on 1 and 115 DF,  p-value: 0.5712

Call:
betareg(formula = MSE_rescaled ~ ethnicity_cat2, data = d_sim_us_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.5746 -0.9836  0.1540  0.9333  1.4319 

Coefficients (mean model with logit link):
                   Estimate Std. Error z value Pr(>|z|)    
(Intercept)        -1.12096    0.07844 -14.290   <2e-16 ***
ethnicity_cat2_POC  0.03417    0.07410   0.461    0.645    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   6.4215     0.8067    7.96 1.72e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 62.87 on 3 Df
Pseudo R-squared: 0.001887
Number of iterations: 13 (BFGS) + 1 (Fisher scoring) 

Education


Call:
lm(formula = scale(MSE) ~ scale(education_catX), data = d_sim_us_adults %>% 
    mutate(education_catX = as.numeric(education_catX)))

Residuals:
    Min      1Q  Median      3Q     Max 
-1.4282 -1.0083 -0.1788  0.9651  1.7513 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)
(Intercept)           -0.00410    0.09322  -0.044    0.965
scale(education_catX) -0.03958    0.09361  -0.423    0.673

Residual standard error: 1.013 on 116 degrees of freedom
  (9 observations deleted due to missingness)
Multiple R-squared:  0.001539,  Adjusted R-squared:  -0.007069 
F-statistic: 0.1788 on 1 and 116 DF,  p-value: 0.6732

Call:
betareg(formula = MSE_rescaled ~ scale(education_catX), data = d_sim_us_adults %>% 
    mutate(education_catX = as.numeric(education_catX)))

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.4538 -0.9727  0.0934  0.9217  1.4091 

Coefficients (mean model with logit link):
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)           -1.12998    0.07862 -14.373   <2e-16 ***
scale(education_catX) -0.02931    0.07426  -0.395    0.693    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   6.3292     0.7921   7.991 1.34e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 63.51 on 3 Df
Pseudo R-squared: 0.00126
Number of iterations: 12 (BFGS) + 2 (Fisher scoring) 


Call:
lm(formula = scale(MSE) ~ education_cat2, data = d_sim_us_adults)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.3937 -0.9960 -0.1715  0.9799  1.7526 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)          0.04839    0.11068   0.437    0.663
education_cat2_coll -0.09678    0.11068  -0.874    0.384

Residual standard error: 1.01 on 116 degrees of freedom
  (9 observations deleted due to missingness)
Multiple R-squared:  0.006549,  Adjusted R-squared:  -0.002015 
F-statistic: 0.7647 on 1 and 116 DF,  p-value: 0.3837

Call:
betareg(formula = MSE_rescaled ~ education_cat2, data = d_sim_us_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.3865 -0.9613  0.1003  0.9406  1.4027 

Coefficients (mean model with logit link):
                    Estimate Std. Error z value Pr(>|z|)    
(Intercept)         -1.09348    0.09074 -12.051   <2e-16 ***
education_cat2_coll -0.06831    0.08721  -0.783    0.433    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   6.3544     0.7954   7.989 1.36e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 63.74 on 3 Df
Pseudo R-squared: 0.005235
Number of iterations: 12 (BFGS) + 2 (Fisher scoring) 

Rural/urban


Call:
lm(formula = scale(MSE) ~ urban_rural_cat2, data = d_sim_us_adults)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.4880 -0.9110 -0.1119  0.9740  1.7746 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)
(Intercept)            -0.03607    0.10621  -0.340    0.735
urban_rural_cat2_rural -0.09736    0.10621  -0.917    0.361

Residual standard error: 0.9975 on 119 degrees of freedom
  (6 observations deleted due to missingness)
Multiple R-squared:  0.007011,  Adjusted R-squared:  -0.001333 
F-statistic: 0.8402 on 1 and 119 DF,  p-value: 0.3612

Call:
betareg(formula = MSE_rescaled ~ urban_rural_cat2, data = d_sim_us_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.5787 -0.8385  0.1561  0.9412  1.4173 

Coefficients (mean model with logit link):
                       Estimate Std. Error z value Pr(>|z|)    
(Intercept)            -1.14201    0.08924 -12.797   <2e-16 ***
urban_rural_cat2_rural -0.05178    0.08547  -0.606    0.545    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   6.4848     0.8011   8.095 5.74e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 65.17 on 3 Df
Pseudo R-squared: 0.003224
Number of iterations: 12 (BFGS) + 2 (Fisher scoring) 

Religion


Call:
lm(formula = scale(MSE) ~ religion_cat3, data = d_sim_us_adults)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.4481 -1.0537 -0.1498  0.9614  1.7193 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)
(Intercept)              0.013403   0.120930   0.111    0.912
religion_cat3_christian -0.003784   0.164282  -0.023    0.982
religion_cat3_other     -0.004167   0.203114  -0.021    0.984

Residual standard error: 1.019 on 109 degrees of freedom
  (15 observations deleted due to missingness)
Multiple R-squared:  3.228e-05, Adjusted R-squared:  -0.01832 
F-statistic: 0.001759 on 2 and 109 DF,  p-value: 0.9982

Call:
betareg(formula = MSE_rescaled ~ religion_cat3, data = d_sim_us_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.4315 -0.9989  0.0756  0.9348  1.3966 

Coefficients (mean model with logit link):
                        Estimate Std. Error z value Pr(>|z|)    
(Intercept)             -1.10031    0.09887 -11.129   <2e-16 ***
religion_cat3_christian  0.01814    0.12942   0.140    0.889    
religion_cat3_other      0.01301    0.16005   0.081    0.935    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   6.2709     0.8045   7.795 6.45e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 59.18 on 4 Df
Pseudo R-squared: 0.0008433
Number of iterations: 15 (BFGS) + 2 (Fisher scoring) 

Target


Call:
lm(formula = scale(MSE) ~ target, data = d_sim_us_adults, contrasts = list(target = "contr.sum"))

Residuals:
    Min      1Q  Median      3Q     Max 
-1.5333 -0.4267 -0.1060  0.5325  2.1092 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.004821   0.069308   0.070 0.944662    
target1     -0.873472   0.205658  -4.247 4.36e-05 ***
target2      0.278033   0.205658   1.352 0.179009    
target3      1.037701   0.213118   4.869 3.54e-06 ***
target4      0.094057   0.205658   0.457 0.648269    
target5     -0.111238   0.205658  -0.541 0.589613    
target6     -0.660440   0.205658  -3.211 0.001706 ** 
target7     -1.147288   0.213118  -5.383 3.82e-07 ***
target8      0.721863   0.213118   3.387 0.000963 ***
target9      0.371713   0.205658   1.807 0.073264 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.7805 on 117 degrees of freedom
Multiple R-squared:  0.4343,    Adjusted R-squared:  0.3908 
F-statistic:  9.98 on 9 and 117 DF,  p-value: 2.949e-11

Call:
betareg(formula = MSE_rescaled ~ target, data = d_sim_us_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.3419 -0.5768 -0.0069  0.6897  2.0609 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.19369    0.06117 -19.513  < 2e-16 ***
target1     -0.74992    0.19730  -3.801 0.000144 ***
target2      0.30231    0.16166   1.870 0.061487 .  
target3      0.85115    0.15865   5.365 8.10e-08 ***
target4      0.11247    0.16634   0.676 0.498942    
target5     -0.10809    0.17287  -0.625 0.531769    
target6     -0.42676    0.18414  -2.318 0.020470 *  
target7     -1.12955    0.22184  -5.092 3.55e-07 ***
target8      0.64964    0.16091   4.037 5.40e-05 ***
target9      0.29374    0.16185   1.815 0.069547 .  

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   11.617      1.434   8.101 5.44e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 104.9 on 11 Df
Pseudo R-squared: 0.4744
Number of iterations: 20 (BFGS) + 2 (Fisher scoring) 

All together


Call:
lm(formula = scale(MSE) ~ scale(age) + gender + scale(education_catX) + 
    ethnicity_cat2 + religion_cat3 + urban_rural_cat2 + target, 
    data = d_sim_us_adults %>% mutate(education_catX = as.numeric(education_catX)))

Residuals:
     Min       1Q   Median       3Q      Max 
-1.49646 -0.48825  0.01192  0.52053  1.91945 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)             -0.16833    0.11476  -1.467 0.146013    
scale(age)               0.09620    0.08764   1.098 0.275393    
gender_m                 0.18959    0.08575   2.211 0.029663 *  
scale(education_catX)   -0.02017    0.08699  -0.232 0.817224    
ethnicity_cat2_POC       0.09782    0.08527   1.147 0.254465    
religion_cat3_christian  0.15160    0.14212   1.067 0.289053    
religion_cat3_other     -0.20335    0.16734  -1.215 0.227573    
urban_rural_cat2_rural  -0.14730    0.10039  -1.467 0.145896    
target1                 -0.88628    0.22850  -3.879 0.000204 ***
target2                  0.15175    0.25431   0.597 0.552233    
target3                  0.96683    0.23034   4.197 6.49e-05 ***
target4                  0.13816    0.25225   0.548 0.585274    
target5                 -0.15699    0.23782  -0.660 0.510931    
target6                 -0.58680    0.23668  -2.479 0.015093 *  
target7                 -1.21028    0.24156  -5.010 2.82e-06 ***
target8                  0.73389    0.24395   3.008 0.003434 ** 
target9                  0.63267    0.25004   2.530 0.013198 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.7832 on 87 degrees of freedom
  (23 observations deleted due to missingness)
Multiple R-squared:  0.502, Adjusted R-squared:  0.4104 
F-statistic: 5.481 on 16 and 87 DF,  p-value: 6.894e-08

Call:
lm(formula = scale(MSE) ~ scale(age) + gender + education_cat2 + 
    ethnicity_cat2 + religion_cat3 + urban_rural_cat2 + target, 
    data = d_sim_us_adults)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.49828 -0.48186  0.00609  0.50569  1.95093 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)             -0.12682    0.13073  -0.970  0.33470    
scale(age)               0.09832    0.08646   1.137  0.25859    
gender_m                 0.18479    0.08484   2.178  0.03210 *  
education_cat2_coll     -0.06855    0.09829  -0.697  0.48740    
ethnicity_cat2_POC       0.09581    0.08437   1.136  0.25925    
religion_cat3_christian  0.15947    0.14214   1.122  0.26499    
religion_cat3_other     -0.20625    0.16611  -1.242  0.21771    
urban_rural_cat2_rural  -0.14099    0.09901  -1.424  0.15800    
target1                 -0.90207    0.22632  -3.986  0.00014 ***
target2                  0.13824    0.25151   0.550  0.58397    
target3                  0.97767    0.22819   4.285 4.71e-05 ***
target4                  0.15971    0.25369   0.630  0.53063    
target5                 -0.16960    0.23755  -0.714  0.47717    
target6                 -0.58222    0.23608  -2.466  0.01562 *  
target7                 -1.20051    0.24038  -4.994 3.01e-06 ***
target8                  0.71676    0.24266   2.954  0.00404 ** 
target9                  0.63535    0.24946   2.547  0.01263 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.7812 on 87 degrees of freedom
  (23 observations deleted due to missingness)
Multiple R-squared:  0.5044,    Adjusted R-squared:  0.4133 
F-statistic: 5.535 on 16 and 87 DF,  p-value: 5.74e-08
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(MSE) ~ scale(age) + gender + scale(education_catX) + ethnicity_cat2 +  
    religion_cat3 + urban_rural_cat2 + +(1 | target)
   Data: d_sim_us_adults %>% mutate(education_catX = as.numeric(education_catX))

REML criterion at convergence: 278.9

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-1.92364 -0.65918 -0.02763  0.66739  2.31261 

Random effects:
 Groups   Name        Variance Std.Dev.
 target   (Intercept) 0.4546   0.6742  
 Residual             0.6127   0.7828  
Number of obs: 104, groups:  target, 10

Fixed effects:
                        Estimate Std. Error       df t value Pr(>|t|)  
(Intercept)             -0.16244    0.24195 11.62404  -0.671   0.5151  
scale(age)               0.10379    0.08716 88.82348   1.191   0.2369  
gender_m                 0.18457    0.08504 89.68817   2.170   0.0326 *
scale(education_catX)   -0.01849    0.08609 90.26392  -0.215   0.8304  
ethnicity_cat2_POC       0.09933    0.08474 89.04396   1.172   0.2443  
religion_cat3_christian  0.14054    0.14141 88.66858   0.994   0.3230  
religion_cat3_other     -0.18618    0.16653 88.62031  -1.118   0.2666  
urban_rural_cat2_rural  -0.14549    0.09983 88.83638  -1.457   0.1485  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) scl(g) gndr_m sc(_X) e_2_PO rlgn_ct3_c rlgn_ct3_t
scale(age)  -0.001                                                  
gender_m    -0.090 -0.038                                           
scl(dctn_X) -0.089 -0.168  0.187                                    
ethnc_2_POC -0.055  0.138  0.070  0.147                             
rlgn_ct3_ch -0.083 -0.160  0.111  0.017  0.185                      
rlgn_ct3_th  0.220  0.016 -0.044 -0.107 -0.135 -0.719               
urbn_rrl_2_  0.245  0.065 -0.167 -0.217 -0.056 -0.174      0.115    
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(MSE) ~ scale(age) + gender + education_cat2 + ethnicity_cat2 +  
    religion_cat3 + urban_rural_cat2 + +(1 | target)
   Data: d_sim_us_adults

REML criterion at convergence: 278.2

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-1.85218 -0.67475 -0.04646  0.66937  2.35719 

Random effects:
 Groups   Name        Variance Std.Dev.
 target   (Intercept) 0.4558   0.6751  
 Residual             0.6097   0.7809  
Number of obs: 104, groups:  target, 10

Fixed effects:
                        Estimate Std. Error       df t value Pr(>|t|)  
(Intercept)             -0.12208    0.25010 13.17602  -0.488   0.6335  
scale(age)               0.10595    0.08607 88.51948   1.231   0.2216  
gender_m                 0.17993    0.08419 89.49226   2.137   0.0353 *
education_cat2_coll     -0.06607    0.09751 89.58791  -0.678   0.4998  
ethnicity_cat2_POC       0.09721    0.08392 88.78821   1.158   0.2498  
religion_cat3_christian  0.14791    0.14140 88.73315   1.046   0.2984  
religion_cat3_other     -0.18827    0.16519 88.84221  -1.140   0.2575  
urban_rural_cat2_rural  -0.13926    0.09854 88.58445  -1.413   0.1611  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) scl(g) gndr_m edc_2_ e_2_PO rlgn_ct3_c rlgn_ct3_t
scale(age)   0.009                                                  
gender_m    -0.109 -0.020                                           
edctn_ct2_c -0.266 -0.092  0.142                                    
ethnc_2_POC -0.063  0.158  0.056  0.084                             
rlgn_ct3_ch -0.059 -0.152  0.099 -0.071  0.178                      
rlgn_ct3_th  0.209 -0.001 -0.026 -0.017 -0.122 -0.718               
urbn_rrl_2_  0.263  0.044 -0.152 -0.163 -0.038 -0.160      0.096    

Call:
betareg(formula = MSE_rescaled ~ scale(age) + gender + scale(education_catX) + ethnicity_cat2 + 
    religion_cat3 + urban_rural_cat2 + target, data = d_sim_us_adults %>% mutate(education_catX = as.numeric(education_catX)))

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.8910 -0.5708  0.0955  0.8445  2.2503 

Coefficients (mean model with logit link):
                        Estimate Std. Error z value Pr(>|z|)    
(Intercept)             -1.34960    0.09628 -14.017  < 2e-16 ***
scale(age)               0.08158    0.06953   1.173 0.240667    
gender_m                 0.16689    0.06855   2.435 0.014907 *  
scale(education_catX)   -0.02301    0.06801  -0.338 0.735058    
ethnicity_cat2_POC       0.08825    0.06826   1.293 0.196036    
religion_cat3_christian  0.16149    0.11216   1.440 0.149916    
religion_cat3_other     -0.18048    0.13184  -1.369 0.171034    
urban_rural_cat2_rural  -0.11848    0.08010  -1.479 0.139099    
target1                 -0.79632    0.20866  -3.816 0.000135 ***
target2                  0.22505    0.19332   1.164 0.244363    
target3                  0.80087    0.16617   4.820 1.44e-06 ***
target4                  0.13648    0.19688   0.693 0.488182    
target5                 -0.18789    0.19251  -0.976 0.329067    
target6                 -0.38232    0.20265  -1.887 0.059216 .  
target7                 -1.22749    0.24446  -5.021 5.14e-07 ***
target8                  0.69093    0.17866   3.867 0.000110 ***
target9                  0.56901    0.18429   3.088 0.002018 ** 

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   12.837      1.758   7.304  2.8e-13 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 91.99 on 18 Df
Pseudo R-squared: 0.5404
Number of iterations: 26 (BFGS) + 1 (Fisher scoring) 

Call:
betareg(formula = MSE_rescaled ~ scale(age) + gender + education_cat2 + ethnicity_cat2 + 
    religion_cat3 + urban_rural_cat2 + target, data = d_sim_us_adults %>% mutate(education_catX = as.numeric(education_catX)))

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.7908 -0.5328  0.1031  0.8451  2.2701 

Coefficients (mean model with logit link):
                        Estimate Std. Error z value Pr(>|z|)    
(Intercept)             -1.31252    0.10747 -12.213  < 2e-16 ***
scale(age)               0.08050    0.06891   1.168 0.242731    
gender_m                 0.16163    0.06797   2.378 0.017413 *  
education_cat2_coll     -0.06522    0.07757  -0.841 0.400519    
ethnicity_cat2_POC       0.08660    0.06753   1.282 0.199719    
religion_cat3_christian  0.16854    0.11217   1.502 0.132973    
religion_cat3_other     -0.18544    0.13107  -1.415 0.157119    
urban_rural_cat2_rural  -0.11362    0.07899  -1.438 0.150328    
target1                 -0.81368    0.20741  -3.923 8.74e-05 ***
target2                  0.21952    0.19106   1.149 0.250560    
target3                  0.81165    0.16460   4.931 8.18e-07 ***
target4                  0.15808    0.19825   0.797 0.425246    
target5                 -0.19078    0.19201  -0.994 0.320421    
target6                 -0.38137    0.20226  -1.886 0.059354 .  
target7                 -1.22328    0.24382  -5.017 5.25e-07 ***
target8                  0.67580    0.17805   3.796 0.000147 ***
target9                  0.56767    0.18396   3.086 0.002030 ** 

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   12.915      1.769   7.303 2.82e-13 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 92.29 on 18 Df
Pseudo R-squared: 0.5429
Number of iterations: 27 (BFGS) + 1 (Fisher scoring) 

US children

Age


Call:
lm(formula = scale(MSE) ~ scale(age), data = d_sim_us_children)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.64038 -0.78859 -0.09887  0.63780  2.35655 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept)  0.02249    0.09002   0.250   0.8032  
scale(age)  -0.22944    0.09043  -2.537   0.0126 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9484 on 109 degrees of freedom
  (6 observations deleted due to missingness)
Multiple R-squared:  0.05577,   Adjusted R-squared:  0.04711 
F-statistic: 6.438 on 1 and 109 DF,  p-value: 0.01259

Call:
betareg(formula = MSE_rescaled ~ scale(age), data = d_sim_us_children)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.2633 -0.8050  0.0276  0.7177  2.1586 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.82621    0.05779 -14.298   <2e-16 ***
scale(age)  -0.14536    0.05725  -2.539   0.0111 *  

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   11.743      1.525   7.701 1.35e-14 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 73.48 on 3 Df
Pseudo R-squared: 0.05834
Number of iterations: 14 (BFGS) + 2 (Fisher scoring) 

Gender


Call:
lm(formula = scale(MSE) ~ gender, data = d_sim_us_children)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.60311 -0.82274 -0.08055  0.56064  2.28751 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.006531   0.093753  -0.070    0.945
gender_m    -0.044949   0.093753  -0.479    0.633

Residual standard error: 1.003 on 115 degrees of freedom
Multiple R-squared:  0.001995,  Adjusted R-squared:  -0.006683 
F-statistic: 0.2299 on 1 and 115 DF,  p-value: 0.6325

Call:
betareg(formula = MSE_rescaled ~ gender, data = d_sim_us_children)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.1166 -0.7964  0.0264  0.6006  2.0272 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.83884    0.06017 -13.940   <2e-16 ***
gender_m    -0.01268    0.05907  -0.215     0.83    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   10.378      1.308   7.937 2.07e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 71.37 on 3 Df
Pseudo R-squared: 0.0004117
Number of iterations: 11 (BFGS) + 2 (Fisher scoring) 

Race/ethnicity


Call:
lm(formula = scale(MSE) ~ ethnicity_cat2, data = d_sim_us_children)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.64875 -0.72877 -0.05011  0.49631  2.30261 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)
(Intercept)        -0.02133    0.10457  -0.204    0.839
ethnicity_cat2_POC -0.16071    0.10457  -1.537    0.128

Residual standard error: 0.9403 on 90 degrees of freedom
  (25 observations deleted due to missingness)
Multiple R-squared:  0.02557,   Adjusted R-squared:  0.01475 
F-statistic: 2.362 on 1 and 90 DF,  p-value: 0.1278

Call:
betareg(formula = MSE_rescaled ~ ethnicity_cat2, data = d_sim_us_children)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.2593 -0.6960  0.0715  0.5952  2.1006 

Coefficients (mean model with logit link):
                   Estimate Std. Error z value Pr(>|z|)    
(Intercept)        -0.86005    0.06807 -12.634   <2e-16 ***
ethnicity_cat2_POC -0.09038    0.06701  -1.349    0.177    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)    11.56       1.65   7.006 2.45e-12 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood:  61.7 on 3 Df
Pseudo R-squared: 0.01968
Number of iterations: 13 (BFGS) + 2 (Fisher scoring) 

Religion


Call:
lm(formula = scale(MSE) ~ religion_cat3, data = d_sim_us_children)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.44597 -0.74331 -0.09855  0.42893  2.02047 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)  
(Intercept)             -0.07873    0.10215  -0.771   0.4427  
religion_cat3_christian  0.18832    0.13128   1.435   0.1546  
religion_cat3_other     -0.36717    0.15798  -2.324   0.0222 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.979 on 98 degrees of freedom
  (16 observations deleted due to missingness)
Multiple R-squared:  0.05282,   Adjusted R-squared:  0.03349 
F-statistic: 2.732 on 2 and 98 DF,  p-value: 0.07002

Call:
betareg(formula = MSE_rescaled ~ religion_cat3, data = d_sim_us_children)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.7941 -0.6858  0.0282  0.5214  1.8772 

Coefficients (mean model with logit link):
                        Estimate Std. Error z value Pr(>|z|)    
(Intercept)             -0.89102    0.06637 -13.425   <2e-16 ***
religion_cat3_christian  0.12197    0.08284   1.472   0.1409    
religion_cat3_other     -0.23630    0.10274  -2.300   0.0215 *  

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   11.136      1.514   7.354 1.93e-13 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 65.23 on 4 Df
Pseudo R-squared: 0.05758
Number of iterations: 12 (BFGS) + 2 (Fisher scoring) 

Target


Call:
lm(formula = scale(MSE) ~ target, data = d_sim_us_children, contrasts = list(target = "contr.sum"))

Residuals:
    Min      1Q  Median      3Q     Max 
-1.4564 -0.6476 -0.1414  0.6052  2.2678 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.01645    0.07969  -0.206 0.836891    
target1     -0.35622    0.24507  -1.454 0.149006    
target2      0.16290    0.23577   0.691 0.491109    
target3     -0.04336    0.22759  -0.191 0.849259    
target4     -0.31437    0.23577  -1.333 0.185234    
target5     -0.32376    0.22759  -1.423 0.157780    
target6     -0.39859    0.24507  -1.626 0.106802    
target7     -0.85152    0.25580  -3.329 0.001197 ** 
target8      0.16653    0.24507   0.680 0.498275    
target9      0.84887    0.23577   3.600 0.000483 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8594 on 107 degrees of freedom
Multiple R-squared:  0.3188,    Adjusted R-squared:  0.2615 
F-statistic: 5.563 on 9 and 107 DF,  p-value: 2.82e-06

Call:
betareg(formula = MSE_rescaled ~ target, data = d_sim_us_children)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.0793 -0.7722 -0.0763  0.7537  2.4489 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.87368    0.05131 -17.027  < 2e-16 ***
target1     -0.20200    0.15840  -1.275 0.202233    
target2      0.14060    0.14445   0.973 0.330387    
target3     -0.04654    0.14326  -0.325 0.745286    
target4     -0.22590    0.15302  -1.476 0.139885    
target5     -0.17757    0.14642  -1.213 0.225229    
target6     -0.24992    0.15981  -1.564 0.117866    
target7     -0.60272    0.17970  -3.354 0.000796 ***
target8      0.12712    0.15037   0.845 0.397898    
target9      0.54206    0.13908   3.898 9.72e-05 ***

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   15.255      1.946   7.839 4.55e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 93.59 on 11 Df
Pseudo R-squared: 0.3191
Number of iterations: 19 (BFGS) + 1 (Fisher scoring) 

All together


Call:
lm(formula = scale(MSE) ~ scale(age) + gender + ethnicity_cat2 + 
    religion_cat3 + target, data = d_sim_us_children)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.2274 -0.5835 -0.1807  0.4714  2.1385 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)              0.02034    0.11764   0.173 0.863267    
scale(age)              -0.21334    0.09342  -2.284 0.025836 *  
gender_m                 0.07626    0.10654   0.716 0.476799    
ethnicity_cat2_POC      -0.17057    0.11696  -1.458 0.149793    
religion_cat3_christian  0.01444    0.14471   0.100 0.920839    
religion_cat3_other     -0.06421    0.18296  -0.351 0.726826    
target1                 -0.27972    0.30390  -0.920 0.360925    
target2                  0.07194    0.27838   0.258 0.796924    
target3                  0.29801    0.25626   1.163 0.249328    
target4                  0.13588    0.32133   0.423 0.673854    
target5                 -0.37378    0.29628  -1.262 0.211826    
target6                 -0.55706    0.30929  -1.801 0.076550 .  
target7                 -0.80145    0.31683  -2.530 0.013974 *  
target8                 -0.03422    0.29357  -0.117 0.907578    
target9                  1.00060    0.26667   3.752 0.000388 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8181 on 62 degrees of freedom
  (40 observations deleted due to missingness)
Multiple R-squared:  0.4005,    Adjusted R-squared:  0.2652 
F-statistic: 2.959 on 14 and 62 DF,  p-value: 0.001681
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(MSE) ~ scale(age) + gender + ethnicity_cat2 + religion_cat3 +      +(1 | target)
   Data: d_sim_us_children

REML criterion at convergence: 207.3

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.4061 -0.7133 -0.2225  0.5368  2.3615 

Random effects:
 Groups   Name        Variance Std.Dev.
 target   (Intercept) 0.1930   0.4393  
 Residual             0.6683   0.8175  
Number of obs: 77, groups:  target, 10

Fixed effects:
                        Estimate Std. Error       df t value Pr(>|t|)  
(Intercept)              0.01347    0.18137 11.62032   0.074   0.9421  
scale(age)              -0.22784    0.09187 65.64777  -2.480   0.0157 *
gender_m                 0.03891    0.10262 69.18043   0.379   0.7057  
ethnicity_cat2_POC      -0.17952    0.11230 69.48885  -1.599   0.1145  
religion_cat3_christian  0.01621    0.13990 68.79762   0.116   0.9081  
religion_cat3_other     -0.12055    0.17749 68.24187  -0.679   0.4993  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) scl(g) gndr_m e_2_PO rlgn_ct3_c
scale(age)  -0.053                                
gender_m     0.048 -0.089                         
ethnc_2_POC -0.280  0.026  0.163                  
rlgn_ct3_ch -0.215  0.032 -0.123  0.071           
rlgn_ct3_th  0.264  0.000  0.024 -0.118 -0.609    

Call:
betareg(formula = MSE_rescaled ~ scale(age) + gender + ethnicity_cat2 + religion_cat3 + 
    target, data = d_sim_us_children)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.8497 -0.8327 -0.1505  0.7703  2.7613 

Coefficients (mean model with logit link):
                        Estimate Std. Error z value Pr(>|z|)    
(Intercept)             -0.85837    0.07026 -12.218  < 2e-16 ***
scale(age)              -0.15075    0.05500  -2.741  0.00612 ** 
gender_m                 0.04090    0.06394   0.640  0.52241    
ethnicity_cat2_POC      -0.11247    0.06814  -1.651  0.09882 .  
religion_cat3_christian  0.01392    0.08658   0.161  0.87230    
religion_cat3_other     -0.03952    0.11112  -0.356  0.72211    
target1                 -0.18171    0.18415  -0.987  0.32376    
target2                  0.08593    0.16244   0.529  0.59682    
target3                  0.18538    0.14817   1.251  0.21088    
target4                  0.11564    0.18959   0.610  0.54189    
target5                 -0.22607    0.18211  -1.241  0.21446    
target6                 -0.36906    0.18950  -1.948  0.05147 .  
target7                 -0.63194    0.21537  -2.934  0.00334 ** 
target8                  0.01587    0.17338   0.092  0.92706    
target9                  0.63907    0.14801   4.318 1.58e-05 ***

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   19.646      3.107   6.322 2.57e-10 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 71.22 on 16 Df
Pseudo R-squared: 0.4194
Number of iterations: 25 (BFGS) + 2 (Fisher scoring) 

Ghana

Ghana adults

Age


Call:
lm(formula = scale(MSE) ~ scale(age), data = d_sim_gh_adults)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.2386 -0.8853 -0.3385  0.9651  1.8986 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept) -0.02698    0.08100  -0.333    0.740  
scale(age)  -0.19804    0.08128  -2.437    0.016 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9787 on 144 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.0396,    Adjusted R-squared:  0.03293 
F-statistic: 5.937 on 1 and 144 DF,  p-value: 0.01605

Call:
betareg(formula = MSE_rescaled ~ scale(age), data = d_sim_gh_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.3769 -0.9227  0.0304  0.9398  1.3404 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.36814    0.08410  -16.27   <2e-16 ***
scale(age)  -0.15254    0.07625   -2.00   0.0455 *  

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   4.8692     0.5593   8.705   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 91.73 on 3 Df
Pseudo R-squared: 0.02866
Number of iterations: 9 (BFGS) + 2 (Fisher scoring) 

Gender


Call:
lm(formula = scale(MSE) ~ gender, data = d_sim_gh_adults)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.0996 -0.8040 -0.2985  1.0829  1.7730 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept) -0.01774    0.08162  -0.217   0.8283  
gender_m    -0.14779    0.08162  -1.811   0.0722 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9924 on 148 degrees of freedom
Multiple R-squared:  0.02167,   Adjusted R-squared:  0.01506 
F-statistic: 3.279 on 1 and 148 DF,  p-value: 0.07222

Call:
betareg(formula = MSE_rescaled ~ gender, data = d_sim_gh_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.1690 -0.8288  0.0811  0.9828  1.3189 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.35196    0.08386 -16.122   <2e-16 ***
gender_m    -0.10810    0.07501  -1.441     0.15    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   4.7577     0.5371   8.858   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood:  90.6 on 3 Df
Pseudo R-squared: 0.01649
Number of iterations: 13 (BFGS) + 2 (Fisher scoring) 

Race/ethnicity


Call:
lm(formula = scale(MSE) ~ ethnicity_cat2, data = d_sim_gh_adults)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.0429 -0.8565 -0.3436  1.0072  1.7045 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)
(Intercept)             -0.01987    0.08351  -0.238    0.812
ethnicity_cat2_nonFante -0.09316    0.08351  -1.116    0.266

Residual standard error: 0.9992 on 148 degrees of freedom
Multiple R-squared:  0.00834,   Adjusted R-squared:  0.00164 
F-statistic: 1.245 on 1 and 148 DF,  p-value: 0.2664

Call:
betareg(formula = MSE_rescaled ~ ethnicity_cat2, data = d_sim_gh_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.0985 -0.8757  0.0208  0.9482  1.2950 

Coefficients (mean model with logit link):
                        Estimate Std. Error z value Pr(>|z|)    
(Intercept)             -1.35270    0.08523 -15.871   <2e-16 ***
ethnicity_cat2_nonFante -0.07164    0.07638  -0.938    0.348    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   4.7180     0.5324   8.862   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 90.01 on 3 Df
Pseudo R-squared: 0.00706
Number of iterations: 12 (BFGS) + 1 (Fisher scoring) 

Education


Call:
lm(formula = scale(MSE) ~ scale(education_catX), data = d_sim_gh_adults %>% 
    mutate(education_catX = as.numeric(education_catX)))

Residuals:
    Min      1Q  Median      3Q     Max 
-1.0573 -0.9852 -0.2745  1.0859  1.8021 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)
(Intercept)            0.006507   0.081823    0.08    0.937
scale(education_catX) -0.097731   0.082099   -1.19    0.236

Residual standard error: 0.9988 on 147 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.009548,  Adjusted R-squared:  0.00281 
F-statistic: 1.417 on 1 and 147 DF,  p-value: 0.2358

Call:
betareg(formula = MSE_rescaled ~ scale(education_catX), data = d_sim_gh_adults %>% 
    mutate(education_catX = as.numeric(education_catX)))

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.1354 -1.0310  0.0899  1.0110  1.3492 

Coefficients (mean model with logit link):
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)           -1.33179    0.08327  -15.99   <2e-16 ***
scale(education_catX) -0.08611    0.07556   -1.14    0.254    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   4.7304     0.5353   8.838   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 88.98 on 3 Df
Pseudo R-squared: 0.01024
Number of iterations: 12 (BFGS) + 1 (Fisher scoring) 


Call:
lm(formula = scale(MSE) ~ education_cat2, data = d_sim_gh_adults)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.1159 -0.8587 -0.3149  1.0742  1.8406 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)
(Intercept)        0.01773    0.08185   0.217    0.829
education_cat2_hs -0.12862    0.08185  -1.572    0.118

Residual standard error: 0.9953 on 147 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.01652,   Adjusted R-squared:  0.009833 
F-statistic:  2.47 on 1 and 147 DF,  p-value: 0.1182

Call:
betareg(formula = MSE_rescaled ~ education_cat2, data = d_sim_gh_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.2261 -0.8659  0.0547  0.9816  1.3662 

Coefficients (mean model with logit link):
                  Estimate Std. Error z value Pr(>|z|)    
(Intercept)       -1.32323    0.08306 -15.931   <2e-16 ***
education_cat2_hs -0.11238    0.07457  -1.507    0.132    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   4.7623     0.5391   8.834   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 89.46 on 3 Df
Pseudo R-squared: 0.01759
Number of iterations: 12 (BFGS) + 2 (Fisher scoring) 

Rural/urban


Call:
lm(formula = scale(MSE) ~ urban_rural_cat2, data = d_sim_gh_adults)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.1431 -0.8662 -0.3567  1.0469  1.8466 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)
(Intercept)             0.03508    0.08392   0.418    0.677
urban_rural_cat2_rural  0.13849    0.08392   1.650    0.101

Residual standard error: 0.9943 on 148 degrees of freedom
Multiple R-squared:  0.01807,   Adjusted R-squared:  0.01143 
F-statistic: 2.723 on 1 and 148 DF,  p-value: 0.101

Call:
betareg(formula = MSE_rescaled ~ urban_rural_cat2, data = d_sim_gh_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.2964 -0.8646  0.0106  0.9582  1.3764 

Coefficients (mean model with logit link):
                       Estimate Std. Error z value Pr(>|z|)    
(Intercept)            -1.30662    0.08407 -15.542   <2e-16 ***
urban_rural_cat2_rural  0.13297    0.07610   1.747   0.0806 .  

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   4.7883     0.5408   8.855   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 91.05 on 3 Df
Pseudo R-squared: 0.02284
Number of iterations: 11 (BFGS) + 2 (Fisher scoring) 

Religion

Target


Call:
lm(formula = scale(MSE) ~ target, data = d_sim_gh_adults, contrasts = list(target = "contr.sum"))

Residuals:
    Min      1Q  Median      3Q     Max 
-1.1893 -0.2391  0.0000  0.1387  1.9963 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept) -4.532e-18  4.553e-02   0.000  1.00000    
target1     -9.696e-01  1.366e-01  -7.099 5.78e-11 ***
target2     -9.696e-01  1.366e-01  -7.099 5.78e-11 ***
target3     -2.562e-01  1.366e-01  -1.876  0.06273 .  
target4      1.855e-01  1.366e-01   1.358  0.17661    
target5      5.517e-01  1.366e-01   4.039 8.80e-05 ***
target6      1.246e+00  1.366e-01   9.125 7.07e-16 ***
target7      9.162e-02  1.366e-01   0.671  0.50344    
target8     -9.374e-01  1.366e-01  -6.863 2.00e-10 ***
target9     -4.058e-01  1.366e-01  -2.971  0.00349 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.5576 on 140 degrees of freedom
Multiple R-squared:  0.7079,    Adjusted R-squared:  0.6891 
F-statistic: 37.69 on 9 and 140 DF,  p-value: < 2.2e-16

Call:
betareg(formula = MSE_rescaled ~ target, data = d_sim_gh_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.3635 -0.3226  0.0000  0.3134  2.7090 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.56482    0.05624 -27.822  < 2e-16 ***
target1     -1.13061    0.19385  -5.833 5.46e-09 ***
target2     -1.13061    0.19385  -5.833 5.46e-09 ***
target3     -0.14179    0.15280  -0.928 0.353433    
target4      0.41155    0.13631   3.019 0.002535 ** 
target5      0.76984    0.12911   5.963 2.48e-09 ***
target6      1.29162    0.12366  10.445  < 2e-16 ***
target7      0.12314    0.14412   0.854 0.392858    
target8     -1.07679    0.19147  -5.624 1.87e-08 ***
target9     -0.56177    0.16906  -3.323 0.000891 ***

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)    16.73       1.95    8.58   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 178.4 on 11 Df
Pseudo R-squared: 0.7305
Number of iterations: 21 (BFGS) + 2 (Fisher scoring) 

All together


Call:
lm(formula = scale(MSE) ~ scale(age) + gender + scale(education_catX) + 
    ethnicity_cat2 + urban_rural_cat2 + target, data = d_sim_gh_adults %>% 
    mutate(education_catX = as.numeric(education_catX)))

Residuals:
     Min       1Q   Median       3Q      Max 
-1.12346 -0.31047 -0.02467  0.17093  1.93057 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)             -0.003505   0.049807  -0.070 0.944005    
scale(age)              -0.086753   0.052147  -1.664 0.098598 .  
gender_m                 0.017111   0.049914   0.343 0.732291    
scale(education_catX)   -0.004249   0.055438  -0.077 0.939026    
ethnicity_cat2_nonFante -0.069933   0.052244  -1.339 0.183044    
urban_rural_cat2_rural   0.063068   0.060166   1.048 0.296471    
target1                 -0.873415   0.144950  -6.026 1.62e-08 ***
target2                 -0.977133   0.140261  -6.967 1.46e-10 ***
target3                 -0.243990   0.142003  -1.718 0.088139 .  
target4                  0.149587   0.142735   1.048 0.296582    
target5                  0.553041   0.146034   3.787 0.000232 ***
target6                  1.163982   0.148872   7.819 1.60e-12 ***
target7                  0.132432   0.141017   0.939 0.349408    
target8                 -0.936736   0.144435  -6.486 1.69e-09 ***
target9                 -0.409953   0.142470  -2.877 0.004688 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.5616 on 130 degrees of freedom
  (5 observations deleted due to missingness)
Multiple R-squared:  0.7127,    Adjusted R-squared:  0.6818 
F-statistic: 23.04 on 14 and 130 DF,  p-value: < 2.2e-16

Call:
lm(formula = scale(MSE) ~ scale(age) + gender + education_cat2 + 
    ethnicity_cat2 + urban_rural_cat2 + target, data = d_sim_gh_adults)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.12477 -0.31513 -0.02035  0.16258  1.92649 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)             -0.003320   0.049643  -0.067 0.946776    
scale(age)              -0.088090   0.052181  -1.688 0.093779 .  
gender_m                 0.016507   0.050079   0.330 0.742216    
education_cat2_hs       -0.009189   0.058030  -0.158 0.874432    
ethnicity_cat2_nonFante -0.069994   0.051216  -1.367 0.174097    
urban_rural_cat2_rural   0.060454   0.060542   0.999 0.319869    
target1                 -0.873195   0.144688  -6.035 1.55e-08 ***
target2                 -0.976031   0.140221  -6.961 1.50e-10 ***
target3                 -0.245123   0.142185  -1.724 0.087090 .  
target4                  0.148502   0.142938   1.039 0.300768    
target5                  0.553701   0.145771   3.798 0.000223 ***
target6                  1.160870   0.150178   7.730 2.58e-12 ***
target7                  0.136151   0.143523   0.949 0.344569    
target8                 -0.936657   0.144390  -6.487 1.67e-09 ***
target9                 -0.409053   0.141656  -2.888 0.004547 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.5616 on 130 degrees of freedom
  (5 observations deleted due to missingness)
Multiple R-squared:  0.7127,    Adjusted R-squared:  0.6818 
F-statistic: 23.04 on 14 and 130 DF,  p-value: < 2.2e-16
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(MSE) ~ scale(age) + gender + scale(education_catX) + ethnicity_cat2 +  
    urban_rural_cat2 + +(1 | target)
   Data: d_sim_gh_adults %>% mutate(education_catX = as.numeric(education_catX))

REML criterion at convergence: 294.6

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.0041 -0.5266 -0.0655  0.3215  3.3997 

Random effects:
 Groups   Name        Variance Std.Dev.
 target   (Intercept) 0.7094   0.8423  
 Residual             0.3154   0.5616  
Number of obs: 145, groups:  target, 10

Fixed effects:
                          Estimate Std. Error         df t value Pr(>|t|)  
(Intercept)              -0.003899   0.270967   9.030693  -0.014   0.9888  
scale(age)               -0.091131   0.052056 130.892796  -1.751   0.0824 .
gender_m                  0.013662   0.049860 130.549886   0.274   0.7845  
scale(education_catX)    -0.003250   0.055403 130.321271  -0.059   0.9533  
ethnicity_cat2_nonFante  -0.072511   0.052151 130.907198  -1.390   0.1668  
urban_rural_cat2_rural    0.067259   0.060085 130.686246   1.119   0.2650  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) scl(g) gndr_m sc(_X) et_2_F
scale(age)  -0.011                            
gender_m     0.021 -0.220                     
scl(dctn_X)  0.015 -0.104  0.015              
ethncty_2_F  0.029  0.106 -0.047 -0.221       
urbn_rrl_2_  0.046 -0.250  0.074  0.508 -0.219
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(MSE) ~ scale(age) + gender + education_cat2 + ethnicity_cat2 +  
    urban_rural_cat2 + +(1 | target)
   Data: d_sim_gh_adults

REML criterion at convergence: 294.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.0063 -0.5342 -0.0673  0.3121  3.3914 

Random effects:
 Groups   Name        Variance Std.Dev.
 target   (Intercept) 0.7079   0.8413  
 Residual             0.3154   0.5616  
Number of obs: 145, groups:  target, 10

Fixed effects:
                          Estimate Std. Error         df t value Pr(>|t|)  
(Intercept)              -0.003822   0.270647   9.023536  -0.014   0.9890  
scale(age)               -0.092627   0.052084 130.941922  -1.778   0.0777 .
gender_m                  0.012855   0.050023 130.574497   0.257   0.7976  
education_cat2_hs        -0.011810   0.057958 130.636468  -0.204   0.8388  
ethnicity_cat2_nonFante  -0.072131   0.051127 130.889768  -1.411   0.1607  
urban_rural_cat2_rural    0.062685   0.060469 130.615654   1.037   0.3018  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) scl(g) gndr_m edc_2_ et_2_F
scale(age)  -0.009                            
gender_m     0.020 -0.208                     
edctn_ct2_h  0.003  0.110  0.083              
ethncty_2_F  0.033  0.073 -0.053 -0.102       
urbn_rrl_2_  0.039 -0.139  0.108  0.517 -0.161

Call:
betareg(formula = MSE_rescaled ~ scale(age) + gender + scale(education_catX) + ethnicity_cat2 + 
    urban_rural_cat2 + target, data = d_sim_gh_adults %>% mutate(education_catX = as.numeric(education_catX)))

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.9169 -0.3454 -0.0051  0.3780  2.7670 

Coefficients (mean model with logit link):
                         Estimate Std. Error z value Pr(>|z|)    
(Intercept)             -1.570784   0.059515 -26.393  < 2e-16 ***
scale(age)              -0.076474   0.053960  -1.417  0.15641    
gender_m                 0.033911   0.053190   0.638  0.52376    
scale(education_catX)    0.001766   0.059957   0.029  0.97650    
ethnicity_cat2_nonFante -0.076939   0.056003  -1.374  0.16950    
urban_rural_cat2_rural   0.053495   0.063147   0.847  0.39691    
target1                 -1.048075   0.198822  -5.271 1.35e-07 ***
target2                 -1.155938   0.195639  -5.909 3.45e-09 ***
target3                 -0.105359   0.155715  -0.677  0.49865    
target4                  0.378160   0.141766   2.667  0.00764 ** 
target5                  0.769768   0.136250   5.650 1.61e-08 ***
target6                  1.225444   0.132726   9.233  < 2e-16 ***
target7                  0.157647   0.146766   1.074  0.28276    
target8                 -1.094933   0.199131  -5.499 3.83e-08 ***
target9                 -0.561446   0.171753  -3.269  0.00108 ** 

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   16.991      2.016    8.43   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 174.8 on 16 Df
Pseudo R-squared: 0.7326
Number of iterations: 26 (BFGS) + 2 (Fisher scoring) 

Call:
betareg(formula = MSE_rescaled ~ scale(age) + gender + education_cat2 + ethnicity_cat2 + 
    urban_rural_cat2 + target, data = d_sim_gh_adults %>% mutate(education_catX = as.numeric(education_catX)))

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.9198 -0.3344 -0.0054  0.3677  2.7679 

Coefficients (mean model with logit link):
                         Estimate Std. Error z value Pr(>|z|)    
(Intercept)             -1.570914   0.059406 -26.444  < 2e-16 ***
scale(age)              -0.076646   0.053948  -1.421  0.15540    
gender_m                 0.033760   0.053372   0.633  0.52704    
education_cat2_hs       -0.003045   0.060261  -0.051  0.95970    
ethnicity_cat2_nonFante -0.076097   0.054146  -1.405  0.15990    
urban_rural_cat2_rural   0.050902   0.062320   0.817  0.41405    
target1                 -1.048406   0.198568  -5.280 1.29e-07 ***
target2                 -1.154696   0.195507  -5.906 3.50e-09 ***
target3                 -0.105764   0.155956  -0.678  0.49767    
target4                  0.377334   0.142015   2.657  0.00788 ** 
target5                  0.770512   0.135931   5.668 1.44e-08 ***
target6                  1.224527   0.134262   9.120  < 2e-16 ***
target7                  0.159591   0.149404   1.068  0.28544    
target8                 -1.095167   0.199094  -5.501 3.78e-08 ***
target9                 -0.562289   0.171083  -3.287  0.00101 ** 

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   16.991      2.016    8.43   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 174.8 on 16 Df
Pseudo R-squared: 0.7326
Number of iterations: 25 (BFGS) + 2 (Fisher scoring) 

Ghana children

Age


Call:
lm(formula = scale(MSE) ~ scale(age), data = d_sim_gh_children)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.4019 -0.9130  0.0356  0.9492  1.5357 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.991e-18  8.187e-02   0.000    1.000
scale(age)  3.661e-02  8.214e-02   0.446    0.656

Residual standard error: 1.003 on 148 degrees of freedom
Multiple R-squared:  0.00134,   Adjusted R-squared:  -0.005407 
F-statistic: 0.1986 on 1 and 148 DF,  p-value: 0.6565

Call:
betareg(formula = MSE_rescaled ~ scale(age), data = d_sim_gh_children)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.6645 -0.7918  0.2020  0.9170  1.3511 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.84640    0.06194 -13.664   <2e-16 ***
scale(age)   0.02646    0.06047   0.438    0.662    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)    7.181      0.789   9.102   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 70.61 on 3 Df
Pseudo R-squared: 0.001423
Number of iterations: 12 (BFGS) + 1 (Fisher scoring) 

Gender


Call:
lm(formula = scale(MSE) ~ gender, data = d_sim_gh_children)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.39196 -0.92322  0.04008  0.92426  1.58300 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0006675  0.0820354   0.008    0.994
gender_m    0.0125151  0.0820354   0.153    0.879

Residual standard error: 1.003 on 148 degrees of freedom
Multiple R-squared:  0.0001572, Adjusted R-squared:  -0.006598 
F-statistic: 0.02327 on 1 and 148 DF,  p-value: 0.879

Call:
betareg(formula = MSE_rescaled ~ gender, data = d_sim_gh_children)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.7201 -0.7971  0.2201  0.8948  1.3743 

Coefficients (mean model with logit link):
             Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.846241   0.062057 -13.637   <2e-16 ***
gender_m     0.000961   0.060344   0.016    0.987    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   7.1715     0.7879   9.103   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 70.51 on 3 Df
Pseudo R-squared: 1.744e-06
Number of iterations: 13 (BFGS) + 1 (Fisher scoring) 

Religion

Target


Call:
lm(formula = scale(MSE) ~ target, data = d_sim_gh_children, contrasts = list(target = "contr.sum"))

Residuals:
     Min       1Q   Median       3Q      Max 
-1.83250 -0.38281 -0.01779  0.40059  1.88660 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.01057    0.05575  -0.190  0.84992    
target1     -1.14561    0.16712  -6.855 2.09e-10 ***
target2     -0.38840    0.16712  -2.324  0.02156 *  
target3      0.53385    0.16241   3.287  0.00128 ** 
target4      0.48096    0.16241   2.961  0.00360 ** 
target5      0.33720    0.17234   1.957  0.05238 .  
target6      0.29145    0.16712   1.744  0.08335 .  
target7     -0.90761    0.17234  -5.266 5.13e-07 ***
target8     -0.92673    0.16712  -5.545 1.41e-07 ***
target9      0.49460    0.16712   2.960  0.00362 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.6822 on 140 degrees of freedom
Multiple R-squared:  0.5627,    Adjusted R-squared:  0.5346 
F-statistic: 20.02 on 9 and 140 DF,  p-value: < 2.2e-16

Call:
betareg(formula = MSE_rescaled ~ target, data = d_sim_gh_children)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.5167 -0.4419  0.0127  0.6824  2.5324 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.91960    0.04531 -20.296  < 2e-16 ***
target1     -0.98240    0.15907  -6.176 6.58e-10 ***
target2     -0.33924    0.13679  -2.480 0.013136 *  
target3      0.44120    0.11841   3.726 0.000194 ***
target4      0.42064    0.11862   3.546 0.000391 ***
target5      0.31662    0.12698   2.493 0.012649 *  
target6      0.30578    0.12336   2.479 0.013183 *  
target7     -0.70853    0.15334  -4.621 3.83e-06 ***
target8     -0.73387    0.14951  -4.908 9.18e-07 ***
target9      0.35644    0.12271   2.905 0.003675 ** 

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   16.447      1.863    8.83   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 131.6 on 11 Df
Pseudo R-squared: 0.5768
Number of iterations: 19 (BFGS) + 2 (Fisher scoring) 

All together


Call:
lm(formula = scale(MSE) ~ scale(age) + gender + target, data = d_sim_gh_children)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.99789 -0.33258 -0.04549  0.39728  1.74140 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.01525    0.05570  -0.274  0.78460    
scale(age)  -0.03187    0.05793  -0.550  0.58308    
gender_m    -0.09747    0.06051  -1.611  0.10948    
target1     -1.17793    0.16805  -7.009 9.70e-11 ***
target2     -0.37780    0.16688  -2.264  0.02514 *  
target3      0.50802    0.16330   3.111  0.00227 ** 
target4      0.44961    0.16323   2.754  0.00667 ** 
target5      0.34004    0.17200   1.977  0.05004 .  
target6      0.30205    0.16688   1.810  0.07249 .  
target7     -0.89085    0.17232  -5.170 8.06e-07 ***
target8     -0.97009    0.16890  -5.744 5.67e-08 ***
target9      0.55008    0.17036   3.229  0.00155 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.6807 on 138 degrees of freedom
Multiple R-squared:  0.5709,    Adjusted R-squared:  0.5367 
F-statistic: 16.69 on 11 and 138 DF,  p-value: < 2.2e-16
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(MSE) ~ scale(age) + gender + +(1 | target)
   Data: d_sim_gh_children

REML criterion at convergence: 346.3

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.87483 -0.53742 -0.06315  0.61199  2.54883 

Random effects:
 Groups   Name        Variance Std.Dev.
 target   (Intercept) 0.6162   0.7850  
 Residual             0.4634   0.6807  
Number of obs: 150, groups:  target, 10

Fixed effects:
             Estimate Std. Error        df t value Pr(>|t|)
(Intercept)  -0.01447    0.25441   8.97306  -0.057    0.956
scale(age)   -0.02814    0.05790 138.26565  -0.486    0.628
gender_m     -0.09105    0.06034 139.46246  -1.509    0.134

Correlation of Fixed Effects:
           (Intr) scl(g)
scale(age) 0.003        
gender_m   0.011  0.252 

Call:
betareg(formula = MSE_rescaled ~ scale(age) + gender + target, data = d_sim_gh_children)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.8542 -0.4865 -0.0147  0.7125  2.4770 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.92257    0.04514 -20.439  < 2e-16 ***
scale(age)  -0.01542    0.04480  -0.344 0.730788    
gender_m    -0.06776    0.04665  -1.453 0.146329    
target1     -1.00246    0.15895  -6.307 2.85e-10 ***
target2     -0.32517    0.13587  -2.393 0.016698 *  
target3      0.42091    0.11871   3.546 0.000391 ***
target4      0.39827    0.11885   3.351 0.000805 ***
target5      0.31951    0.12626   2.531 0.011385 *  
target6      0.31074    0.12278   2.531 0.011376 *  
target7     -0.69862    0.15272  -4.574 4.78e-06 ***
target8     -0.76168    0.15010  -5.074 3.89e-07 ***
target9      0.38987    0.12499   3.119 0.001813 ** 

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   16.670      1.888   8.828   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 132.6 on 13 Df
Pseudo R-squared: 0.586
Number of iterations: 20 (BFGS) + 2 (Fisher scoring) 

Thailand

Thailand adults

Age


Call:
lm(formula = scale(MSE) ~ scale(age), data = d_sim_th_adults)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.8227 -0.7161 -0.1706  0.4121  2.3409 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)   
(Intercept) -0.005132   0.080033  -0.064   0.9490   
scale(age)   0.234203   0.080302   2.917   0.0041 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9769 on 147 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.0547,    Adjusted R-squared:  0.04827 
F-statistic: 8.506 on 1 and 147 DF,  p-value: 0.004095

Call:
betareg(formula = MSE_rescaled ~ scale(age), data = d_sim_th_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.3514 -0.6759 -0.0719  0.4979  2.1050 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.80410    0.05074 -15.846  < 2e-16 ***
scale(age)   0.13562    0.04945   2.743  0.00609 ** 

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   11.192      1.252   8.939   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood:  94.7 on 3 Df
Pseudo R-squared: 0.04855
Number of iterations: 13 (BFGS) + 1 (Fisher scoring) 

Gender


Call:
lm(formula = scale(MSE) ~ gender, data = d_sim_th_adults)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.5220 -0.7040 -0.2978  0.4676  2.2403 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept)  -0.3763     0.2408  -1.563   0.1203  
gender_m      0.4819     0.2520   1.912   0.0578 .
gender_o     -0.8133     0.4720  -1.723   0.0870 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9943 on 147 degrees of freedom
Multiple R-squared:  0.02468,   Adjusted R-squared:  0.01141 
F-statistic:  1.86 on 2 and 147 DF,  p-value: 0.1594
contrasts dropped from factor gender due to missing levels

Call:
lm(formula = scale(MSE) ~ gender, data = d_sim_th_adults %>% 
    filter(gender != "other"), contrasts = list(gender = "contr.sum"))

Residuals:
    Min      1Q  Median      3Q     Max 
-1.5274 -0.7182 -0.3045  0.4955  2.2484 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)  0.01429    0.08376   0.171    0.865
gender1     -0.07553    0.08376  -0.902    0.369

Residual standard error: 1.001 on 146 degrees of freedom
Multiple R-squared:  0.005538,  Adjusted R-squared:  -0.001273 
F-statistic: 0.8131 on 1 and 146 DF,  p-value: 0.3687

Call:
betareg(formula = MSE_rescaled ~ gender, data = d_sim_th_adults %>% filter(gender != 
    "other") %>% mutate(gender = case_when(gender == "female" ~ -1, gender == "male" ~ 
    1)))

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.8734 -0.6511 -0.1933  0.5496  1.9995 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.78071    0.05220 -14.956   <2e-16 ***
gender       0.03438    0.05143   0.668    0.504    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   10.843      1.215   8.923   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 91.08 on 3 Df
Pseudo R-squared: 0.003166
Number of iterations: 11 (BFGS) + 2 (Fisher scoring) 

Race/ethnicity

Education


Call:
lm(formula = scale(MSE) ~ scale(education_catX), data = d_sim_th_adults %>% 
    mutate(education_catX = as.numeric(education_catX)))

Residuals:
    Min      1Q  Median      3Q     Max 
-1.6792 -0.6830 -0.2081  0.4750  2.3728 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)  
(Intercept)           -0.03157    0.08104  -0.390   0.6975  
scale(education_catX) -0.18402    0.08132  -2.263   0.0252 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9759 on 143 degrees of freedom
  (5 observations deleted due to missingness)
Multiple R-squared:  0.03457,   Adjusted R-squared:  0.02782 
F-statistic:  5.12 on 1 and 143 DF,  p-value: 0.02515

Call:
betareg(formula = MSE_rescaled ~ scale(education_catX), data = d_sim_th_adults %>% 
    mutate(education_catX = as.numeric(education_catX)))

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.1629 -0.6706 -0.1095  0.5518  2.1262 

Coefficients (mean model with logit link):
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)           -0.82002    0.05155 -15.907   <2e-16 ***
scale(education_catX) -0.10403    0.05061  -2.055   0.0398 *  

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)    11.20       1.27   8.817   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 92.59 on 3 Df
Pseudo R-squared: 0.02906
Number of iterations: 13 (BFGS) + 2 (Fisher scoring) 


Call:
lm(formula = scale(MSE) ~ education_cat2, data = d_sim_th_adults)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.6413 -0.7070 -0.2242  0.4714  2.3545 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)         -0.001235   0.082432  -0.015   0.9881  
education_cat2_coll -0.175928   0.082432  -2.134   0.0345 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9777 on 143 degrees of freedom
  (5 observations deleted due to missingness)
Multiple R-squared:  0.03087,   Adjusted R-squared:  0.02409 
F-statistic: 4.555 on 1 and 143 DF,  p-value: 0.03453

Call:
betareg(formula = MSE_rescaled ~ education_cat2, data = d_sim_th_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.1006 -0.6261 -0.0953  0.5436  2.1057 

Coefficients (mean model with logit link):
                    Estimate Std. Error z value Pr(>|z|)    
(Intercept)         -0.80310    0.05212 -15.408   <2e-16 ***
education_cat2_coll -0.09583    0.05132  -1.867   0.0619 .  

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   11.142      1.263   8.818   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 92.26 on 3 Df
Pseudo R-squared: 0.0246
Number of iterations: 10 (BFGS) + 2 (Fisher scoring) 

Rural/urban


Call:
lm(formula = scale(MSE) ~ urban_rural_cat2, data = d_sim_th_adults)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.4677 -0.7626 -0.2797  0.4761  2.3065 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)
(Intercept)            -0.03534    0.08900  -0.397    0.692
urban_rural_cat2_rural  0.07578    0.08900   0.851    0.396

Residual standard error: 0.991 on 139 degrees of freedom
  (9 observations deleted due to missingness)
Multiple R-squared:  0.005188,  Adjusted R-squared:  -0.001969 
F-statistic: 0.7249 on 1 and 139 DF,  p-value: 0.396

Call:
betareg(formula = MSE_rescaled ~ urban_rural_cat2, data = d_sim_th_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.8613 -0.7207 -0.1704  0.5443  2.0901 

Coefficients (mean model with logit link):
                       Estimate Std. Error z value Pr(>|z|)    
(Intercept)            -0.82040    0.05611 -14.622   <2e-16 ***
urban_rural_cat2_rural  0.04982    0.05529   0.901    0.367    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   11.031      1.268   8.702   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 88.38 on 3 Df
Pseudo R-squared: 0.006162
Number of iterations: 8 (BFGS) + 2 (Fisher scoring) 

Religion

Target


Call:
lm(formula = scale(MSE) ~ target, data = d_sim_th_adults, contrasts = list(target = "contr.sum"))

Residuals:
    Min      1Q  Median      3Q     Max 
-1.5241 -0.7322 -0.1309  0.6421  2.3396 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)   
(Intercept) -3.082e-16  8.053e-02   0.000  1.00000   
target1      2.499e-01  2.416e-01   1.034  0.30281   
target2      2.884e-02  2.416e-01   0.119  0.90515   
target3      1.968e-01  2.416e-01   0.815  0.41672   
target4     -1.156e-01  2.416e-01  -0.478  0.63311   
target5      3.514e-01  2.416e-01   1.454  0.14806   
target6     -1.622e-01  2.416e-01  -0.672  0.50300   
target7     -6.329e-01  2.416e-01  -2.620  0.00977 **
target8      1.830e-01  2.416e-01   0.758  0.44997   
target9      2.243e-01  2.416e-01   0.929  0.35474   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9863 on 140 degrees of freedom
Multiple R-squared:  0.08599,   Adjusted R-squared:  0.02723 
F-statistic: 1.464 on 9 and 140 DF,  p-value: 0.1672

Call:
betareg(formula = MSE_rescaled ~ target, data = d_sim_th_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.8707 -0.7335 -0.0492  0.7037  2.2438 

Coefficients (mean model with logit link):
             Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.803257   0.049709 -16.159   <2e-16 ***
target1      0.178585   0.143284   1.246   0.2126    
target2     -0.008229   0.146551  -0.056   0.9552    
target3      0.111397   0.144360   0.772   0.4403    
target4     -0.127666   0.149085  -0.856   0.3918    
target5      0.216832   0.142722   1.519   0.1287    
target6     -0.119940   0.148911  -0.805   0.4206    
target7     -0.380804   0.155562  -2.448   0.0144 *  
target8      0.146666   0.143782   1.020   0.3077    
target9      0.132666   0.144008   0.921   0.3569    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   11.663      1.302   8.956   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 98.26 on 11 Df
Pseudo R-squared: 0.08959
Number of iterations: 20 (BFGS) + 2 (Fisher scoring) 

All together


Call:
lm(formula = scale(MSE) ~ scale(age) + gender + scale(education_catX) + 
    urban_rural_cat2 + target, data = d_sim_th_adults %>% filter(gender != 
    "other") %>% mutate(education_catX = as.numeric(education_catX), 
    gender = case_when(gender == "female" ~ -1, gender == "male" ~ 
        1)))

Residuals:
     Min       1Q   Median       3Q      Max 
-1.67271 -0.63947 -0.06236  0.46574  2.46055 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)  
(Intercept)            -0.039643   0.089408  -0.443   0.6583  
scale(age)              0.188059   0.100709   1.867   0.0643 .
gender                  0.038295   0.090801   0.422   0.6740  
scale(education_catX)  -0.106247   0.102039  -1.041   0.2999  
urban_rural_cat2_rural -0.003657   0.092223  -0.040   0.9684  
target1                 0.272652   0.249196   1.094   0.2761  
target2                -0.129426   0.264834  -0.489   0.6259  
target3                 0.345354   0.239768   1.440   0.1524  
target4                -0.009965   0.246891  -0.040   0.9679  
target5                 0.283155   0.254478   1.113   0.2681  
target6                -0.072453   0.246277  -0.294   0.7691  
target7                -0.499755   0.244246  -2.046   0.0429 *
target8                 0.224240   0.246431   0.910   0.3647  
target9                -0.114037   0.287353  -0.397   0.6922  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9548 on 120 degrees of freedom
  (14 observations deleted due to missingness)
Multiple R-squared:  0.1403,    Adjusted R-squared:  0.04712 
F-statistic: 1.506 on 13 and 120 DF,  p-value: 0.1247

Call:
lm(formula = scale(MSE) ~ scale(age) + gender + education_cat2 + 
    urban_rural_cat2 + target, data = d_sim_th_adults %>% filter(gender != 
    "other") %>% mutate(education_catX = as.numeric(education_catX), 
    gender = case_when(gender == "female" ~ -1, gender == "male" ~ 
        1)))

Residuals:
     Min       1Q   Median       3Q      Max 
-1.64313 -0.61016 -0.03672  0.45523  2.43583 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)  
(Intercept)            -0.024350   0.093046  -0.262   0.7940  
scale(age)              0.199076   0.105015   1.896   0.0604 .
gender                  0.034180   0.090981   0.376   0.7078  
education_cat2_coll    -0.078892   0.109726  -0.719   0.4735  
urban_rural_cat2_rural -0.005729   0.094139  -0.061   0.9516  
target1                 0.270855   0.251209   1.078   0.2831  
target2                -0.128616   0.269188  -0.478   0.6337  
target3                 0.338462   0.240205   1.409   0.1614  
target4                -0.026638   0.246474  -0.108   0.9141  
target5                 0.289487   0.254977   1.135   0.2585  
target6                -0.069188   0.246847  -0.280   0.7797  
target7                -0.497339   0.245189  -2.028   0.0447 *
target8                 0.218400   0.246959   0.884   0.3783  
target9                -0.101148   0.287542  -0.352   0.7256  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.957 on 120 degrees of freedom
  (14 observations deleted due to missingness)
Multiple R-squared:  0.1362,    Adjusted R-squared:  0.04264 
F-statistic: 1.456 on 13 and 120 DF,  p-value: 0.1443
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(MSE) ~ scale(age) + gender + scale(education_catX) + urban_rural_cat2 +  
    +(1 | target)
   Data: 
d_sim_th_adults %>% filter(gender != "other") %>% mutate(education_catX = as.numeric(education_catX),  
    gender = case_when(gender == "female" ~ -1, gender == "male" ~          1))

REML criterion at convergence: 379.4

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.6687 -0.7160 -0.1924  0.3970  2.5214 

Random effects:
 Groups   Name        Variance Std.Dev.
 target   (Intercept) 0.01596  0.1263  
 Residual             0.90806  0.9529  
Number of obs: 134, groups:  target, 10

Fixed effects:
                         Estimate Std. Error         df t value Pr(>|t|)  
(Intercept)             -0.038622   0.097341  11.806262  -0.397   0.6986  
scale(age)               0.195337   0.098909 126.599653   1.975   0.0505 .
gender                   0.027442   0.085586 125.968517   0.321   0.7490  
scale(education_catX)   -0.083493   0.098973 128.583490  -0.844   0.4005  
urban_rural_cat2_rural   0.002382   0.090489 126.740314   0.026   0.9790  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) scl(g) gender sc(_X)
scale(age)   0.017                     
gender       0.122 -0.098              
scl(dctn_X) -0.036  0.507 -0.028       
urbn_rrl_2_ -0.302 -0.078  0.102  0.133
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(MSE) ~ scale(age) + gender + education_cat2 + urban_rural_cat2 +  
    +(1 | target)
   Data: 
d_sim_th_adults %>% filter(gender != "other") %>% mutate(education_catX = as.numeric(education_catX),  
    gender = case_when(gender == "female" ~ -1, gender == "male" ~          1))

REML criterion at convergence: 379.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.7145 -0.7209 -0.1902  0.4080  2.4959 

Random effects:
 Groups   Name        Variance Std.Dev.
 target   (Intercept) 0.01459  0.1208  
 Residual             0.91206  0.9550  
Number of obs: 134, groups:  target, 10

Fixed effects:
                         Estimate Std. Error         df t value Pr(>|t|)  
(Intercept)             -0.028199   0.099866  13.347738  -0.282   0.7820  
scale(age)               0.206975   0.102633 127.561797   2.017   0.0458 *
gender                   0.024397   0.085675 125.726454   0.285   0.7763  
education_cat2_coll     -0.056640   0.105293 128.975954  -0.538   0.5915  
urban_rural_cat2_rural   0.002019   0.091958 127.607802   0.022   0.9825  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) scl(g) gender edc_2_
scale(age)  -0.105                     
gender       0.114 -0.071              
edctn_ct2_c -0.248  0.555  0.019       
urbn_rrl_2_ -0.339 -0.020  0.109  0.212

Call:
betareg(formula = MSE_rescaled ~ scale(age) + gender + scale(education_catX) + urban_rural_cat2 + 
    target, data = d_sim_th_adults %>% filter(gender != "other") %>% mutate(education_catX = as.numeric(education_catX), 
    gender = case_when(gender == "female" ~ -1, gender == "male" ~ 1)))

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.0637 -0.6569 -0.0149  0.5831  2.4739 

Coefficients (mean model with logit link):
                        Estimate Std. Error z value Pr(>|z|)    
(Intercept)            -0.821619   0.054327 -15.124   <2e-16 ***
scale(age)              0.114569   0.059649   1.921   0.0548 .  
gender                  0.015689   0.054344   0.289   0.7728    
scale(education_catX)  -0.061299   0.061070  -1.004   0.3155    
urban_rural_cat2_rural -0.005483   0.055405  -0.099   0.9212    
target1                 0.176979   0.146268   1.210   0.2263    
target2                -0.124730   0.160882  -0.775   0.4382    
target3                 0.204585   0.141089   1.450   0.1470    
target4                -0.065861   0.148931  -0.442   0.6583    
target5                 0.189076   0.148649   1.272   0.2034    
target6                -0.051661   0.148111  -0.349   0.7272    
target7                -0.293506   0.153736  -1.909   0.0562 .  
target8                 0.164382   0.144564   1.137   0.2555    
target9                -0.055437   0.171422  -0.323   0.7464    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   13.058      1.548   8.434   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 95.14 on 15 Df
Pseudo R-squared: 0.1334
Number of iterations: 23 (BFGS) + 2 (Fisher scoring) 

Call:
betareg(formula = MSE_rescaled ~ scale(age) + gender + education_cat2 + urban_rural_cat2 + 
    target, data = d_sim_th_adults %>% filter(gender != "other") %>% mutate(education_catX = as.numeric(education_catX), 
    gender = case_when(gender == "female" ~ -1, gender == "male" ~ 1)))

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.0464 -0.6345  0.0231  0.5641  2.4493 

Coefficients (mean model with logit link):
                        Estimate Std. Error z value Pr(>|z|)    
(Intercept)            -0.813684   0.056382 -14.432   <2e-16 ***
scale(age)              0.122737   0.062113   1.976   0.0482 *  
gender                  0.012987   0.054445   0.239   0.8115    
education_cat2_coll    -0.041505   0.065663  -0.632   0.5273    
urban_rural_cat2_rural -0.005253   0.056586  -0.093   0.9260    
target1                 0.174669   0.147408   1.185   0.2360    
target2                -0.123914   0.163377  -0.758   0.4482    
target3                 0.200468   0.141291   1.419   0.1559    
target4                -0.077748   0.148624  -0.523   0.6009    
target5                 0.192875   0.148814   1.296   0.1949    
target6                -0.047675   0.148346  -0.321   0.7479    
target7                -0.291774   0.154209  -1.892   0.0585 .  
target8                 0.160173   0.144838   1.106   0.2688    
target9                -0.047379   0.171469  -0.276   0.7823    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   12.997      1.541   8.435   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 94.83 on 15 Df
Pseudo R-squared: 0.1296
Number of iterations: 24 (BFGS) + 2 (Fisher scoring) 

Thailand children

Age


Call:
lm(formula = scale(MSE) ~ scale(age), data = d_sim_th_children)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.5399 -0.7272 -0.2931  0.7147  2.9228 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)
(Intercept)  4.658e-17  8.100e-02   0.000    1.000
scale(age)  -9.654e-02  8.127e-02  -1.188    0.237

Residual standard error: 0.9986 on 150 degrees of freedom
Multiple R-squared:  0.00932,   Adjusted R-squared:  0.002716 
F-statistic: 1.411 on 1 and 150 DF,  p-value: 0.2367

Call:
betareg(formula = MSE_rescaled ~ scale(age), data = d_sim_th_children)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.9606 -0.6910 -0.1969  0.7630  2.6049 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.93266    0.04237  -22.01   <2e-16 ***
scale(age)  -0.05117    0.04194   -1.22    0.222    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   17.059      1.913   8.916   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 129.1 on 3 Df
Pseudo R-squared: 0.01085
Number of iterations: 9 (BFGS) + 2 (Fisher scoring) 

Gender


Call:
lm(formula = scale(MSE) ~ gender, data = d_sim_th_children)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.6021 -0.7579 -0.1796  0.7213  3.1169 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.008783   0.081551   0.108    0.914
gender_m    0.083441   0.081551   1.023    0.308

Residual standard error: 0.9998 on 150 degrees of freedom
Multiple R-squared:  0.006931,  Adjusted R-squared:  0.0003104 
F-statistic: 1.047 on 1 and 150 DF,  p-value: 0.3079

Call:
betareg(formula = MSE_rescaled ~ gender, data = d_sim_th_children)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.0166 -0.7391 -0.0938  0.7622  2.7135 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.92876    0.04264 -21.782   <2e-16 ***
gender_m     0.03456    0.04205   0.822    0.411    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   16.964      1.902   8.918   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 128.7 on 3 Df
Pseudo R-squared: 0.004708
Number of iterations: 12 (BFGS) + 2 (Fisher scoring) 

Religion

Target


Call:
lm(formula = scale(MSE) ~ target, data = d_sim_th_children, contrasts = list(target = "contr.sum"))

Residuals:
    Min      1Q  Median      3Q     Max 
-1.5939 -0.6810 -0.2017  0.4187  2.8156 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)  
(Intercept) -0.001869   0.078903  -0.024   0.9811  
target1     -0.022011   0.238036  -0.092   0.9265  
target2     -0.231547   0.231320  -1.001   0.3185  
target3     -0.019541   0.238036  -0.082   0.9347  
target4     -0.276511   0.238036  -1.162   0.2473  
target5     -0.352161   0.238036  -1.479   0.1412  
target6     -0.109907   0.238036  -0.462   0.6450  
target7     -0.399233   0.238036  -1.677   0.0957 .
target8      0.452051   0.238036   1.899   0.0596 .
target9      0.515583   0.231320   2.229   0.0274 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9725 on 142 degrees of freedom
Multiple R-squared:  0.1107,    Adjusted R-squared:  0.05433 
F-statistic: 1.964 on 9 and 142 DF,  p-value: 0.04774

Call:
betareg(formula = MSE_rescaled ~ target, data = d_sim_th_children)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.8769 -0.7085 -0.1184  0.5032  2.7180 

Coefficients (mean model with logit link):
             Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.939353   0.040478 -23.206   <2e-16 ***
target1      0.001302   0.120112   0.011   0.9914    
target2     -0.096821   0.118742  -0.815   0.4148    
target3     -0.016916   0.120483  -0.140   0.8883    
target4     -0.163343   0.123739  -1.320   0.1868    
target5     -0.168808   0.123870  -1.363   0.1729    
target6     -0.048193   0.121139  -0.398   0.6908    
target7     -0.232307   0.125437  -1.852   0.0640 .  
target8      0.231012   0.116053   1.991   0.0465 *  
target9      0.237074   0.112724   2.103   0.0355 *  

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   19.003      2.136   8.895   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 137.2 on 11 Df
Pseudo R-squared: 0.116
Number of iterations: 18 (BFGS) + 2 (Fisher scoring) 

All together


Call:
lm(formula = scale(MSE) ~ scale(age) + gender + target, data = d_sim_th_children)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7560 -0.6538 -0.2126  0.5222  2.9321 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)  
(Intercept)  0.005421   0.079310   0.068   0.9456  
scale(age)  -0.103910   0.079957  -1.300   0.1959  
gender_m     0.068863   0.080833   0.852   0.3957  
target1     -0.047832   0.239150  -0.200   0.8418  
target2     -0.237626   0.231949  -1.024   0.3074  
target3     -0.010505   0.238525  -0.044   0.9649  
target4     -0.283076   0.238106  -1.189   0.2365  
target5     -0.333051   0.238144  -1.399   0.1642  
target6     -0.094453   0.238499  -0.396   0.6927  
target7     -0.389305   0.238023  -1.636   0.1042  
target8      0.458324   0.237920   1.926   0.0561 .
target9      0.515521   0.232123   2.221   0.0280 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9715 on 140 degrees of freedom
Multiple R-squared:  0.1249,    Adjusted R-squared:  0.05617 
F-statistic: 1.817 on 11 and 140 DF,  p-value: 0.05638
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(MSE) ~ scale(age) + gender + +(1 | target)
   Data: d_sim_th_children

REML criterion at convergence: 435.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.5498 -0.7577 -0.2870  0.6243  3.0218 

Random effects:
 Groups   Name        Variance Std.Dev.
 target   (Intercept) 0.05678  0.2383  
 Residual             0.94357  0.9714  
Number of obs: 152, groups:  target, 10

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept)   0.007629   0.109367   9.128123   0.070    0.946
scale(age)   -0.103718   0.079594 142.376071  -1.303    0.195
gender_m      0.080764   0.080103 144.424864   1.008    0.315

Correlation of Fixed Effects:
           (Intr) scl(g)
scale(age) -0.005       
gender_m    0.078 -0.072

Call:
betareg(formula = MSE_rescaled ~ scale(age) + gender + target, data = d_sim_th_children)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.1108 -0.7106 -0.1652  0.6362  2.8274 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.93763    0.04040 -23.206   <2e-16 ***
scale(age)  -0.05734    0.04014  -1.428   0.1532    
gender_m     0.02530    0.04055   0.624   0.5327    
target1     -0.01090    0.11989  -0.091   0.9276    
target2     -0.09795    0.11838  -0.827   0.4080    
target3     -0.01588    0.12007  -0.132   0.8948    
target4     -0.16828    0.12307  -1.367   0.1715    
target5     -0.15906    0.12317  -1.291   0.1966    
target6     -0.04316    0.12067  -0.358   0.7206    
target7     -0.23011    0.12479  -1.844   0.0652 .  
target8      0.23550    0.11528   2.043   0.0411 *  
target9      0.24164    0.11245   2.149   0.0316 *  

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   19.303      2.171   8.892   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 138.4 on 13 Df
Pseudo R-squared: 0.1297
Number of iterations: 22 (BFGS) + 1 (Fisher scoring) 

China

China adults

Age


Call:
lm(formula = scale(MSE) ~ scale(age), data = d_sim_ch_adults)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.98225 -0.96533 -0.08779  0.84375  1.81946 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)
(Intercept)  1.087e-16  8.600e-02   0.000    1.000
scale(age)  -4.124e-02  8.631e-02  -0.478    0.634

Residual standard error: 1.003 on 134 degrees of freedom
Multiple R-squared:  0.001701,  Adjusted R-squared:  -0.005749 
F-statistic: 0.2283 on 1 and 134 DF,  p-value: 0.6336

Call:
betareg(formula = MSE_rescaled ~ scale(age), data = d_sim_ch_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.3855 -0.8880  0.0707  0.8367  1.5746 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.81782    0.05788 -14.131   <2e-16 ***
scale(age)  -0.02482    0.05701  -0.435    0.663    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)    9.263      1.078   8.593   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 76.02 on 3 Df
Pseudo R-squared: 0.001559
Number of iterations: 12 (BFGS) + 2 (Fisher scoring) 

Gender


Call:
lm(formula = scale(MSE) ~ gender, data = d_sim_ch_adults)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.1027 -0.9249 -0.1069  0.8721  1.8977 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)  0.01600    0.08581   0.186    0.852
gender_m     0.13607    0.08581   1.586    0.115

Residual standard error: 0.9948 on 133 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.01855,   Adjusted R-squared:  0.01117 
F-statistic: 2.514 on 1 and 133 DF,  p-value: 0.1152

Call:
betareg(formula = MSE_rescaled ~ gender, data = d_sim_ch_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.6655 -0.8361  0.0343  0.8627  1.6182 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.80970    0.05793 -13.978   <2e-16 ***
gender_m     0.06867    0.05682   1.209    0.227    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)     9.33       1.09    8.56   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood:  75.8 on 3 Df
Pseudo R-squared: 0.0105
Number of iterations: 10 (BFGS) + 2 (Fisher scoring) 

Race/ethnicity

Education


Call:
lm(formula = scale(MSE) ~ scale(education_catX), data = d_sim_ch_adults %>% 
    mutate(education_catX = as.numeric(education_catX)))

Residuals:
     Min       1Q   Median       3Q      Max 
-1.83531 -0.97463 -0.09368  0.86909  1.90642 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)
(Intercept)            2.856e-05  8.528e-02   0.000     1.00
scale(education_catX) -1.377e-01  8.560e-02  -1.608     0.11

Residual standard error: 0.9871 on 132 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.01922,   Adjusted R-squared:  0.01179 
F-statistic: 2.587 on 1 and 132 DF,  p-value: 0.1102

Call:
betareg(formula = MSE_rescaled ~ scale(education_catX), data = d_sim_ch_adults %>% 
    mutate(education_catX = as.numeric(education_catX)))

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.3273 -0.9174  0.0886  0.8640  1.6836 

Coefficients (mean model with logit link):
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)           -0.81960    0.05741 -14.277   <2e-16 ***
scale(education_catX) -0.10514    0.05630  -1.867   0.0619 .  

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)    9.622      1.130   8.517   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 77.22 on 3 Df
Pseudo R-squared: 0.0242
Number of iterations: 13 (BFGS) + 1 (Fisher scoring) 


Call:
lm(formula = scale(MSE) ~ education_cat2, data = d_sim_ch_adults)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.80302 -0.94233 -0.08729  0.89331  1.93871 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)  
(Intercept)          0.02294    0.08562   0.268   0.7891  
education_cat2_coll -0.17058    0.08562  -1.992   0.0484 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9821 on 132 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.0292,    Adjusted R-squared:  0.02184 
F-statistic:  3.97 on 1 and 132 DF,  p-value: 0.04839

Call:
betareg(formula = MSE_rescaled ~ education_cat2, data = d_sim_ch_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.2946 -0.8818  0.0835  0.8620  1.7224 

Coefficients (mean model with logit link):
                    Estimate Std. Error z value Pr(>|z|)    
(Intercept)         -0.80308    0.05727 -14.022   <2e-16 ***
education_cat2_coll -0.13018    0.05627  -2.313   0.0207 *  

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)    9.755      1.146   8.512   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 78.14 on 3 Df
Pseudo R-squared: 0.0373
Number of iterations: 12 (BFGS) + 2 (Fisher scoring) 

Rural/urban


Call:
lm(formula = scale(MSE) ~ urban_rural_cat2, data = d_sim_ch_adults)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.9687 -1.0190 -0.1139  0.8448  1.8166 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)
(Intercept)            -0.003714   0.087560  -0.042    0.966
urban_rural_cat2_rural -0.021766   0.087560  -0.249    0.804

Residual standard error: 1.007 on 133 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.0004644, Adjusted R-squared:  -0.007051 
F-statistic: 0.06179 on 1 and 133 DF,  p-value: 0.8041

Call:
betareg(formula = MSE_rescaled ~ urban_rural_cat2, data = d_sim_ch_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.4549 -0.9255  0.0585  0.8343  1.5591 

Coefficients (mean model with logit link):
                        Estimate Std. Error z value Pr(>|z|)    
(Intercept)            -0.818885   0.058878 -13.908   <2e-16 ***
urban_rural_cat2_rural -0.005068   0.057716  -0.088     0.93    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)    9.188      1.073   8.564   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 75.02 on 3 Df
Pseudo R-squared: 5.639e-05
Number of iterations: 12 (BFGS) + 2 (Fisher scoring) 

Religion


Call:
lm(formula = scale(MSE) ~ religion_cat3, data = d_sim_ch_adults)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.92818 -0.94171 -0.04716  0.81335  1.78728 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)
(Intercept)              0.1581     0.1378   1.147    0.254
religion_cat3_buddhist  -0.1663     0.1883  -0.883    0.379
religion_cat3_other      0.3205     0.2357   1.360    0.177

Residual standard error: 0.994 on 111 degrees of freedom
  (22 observations deleted due to missingness)
Multiple R-squared:  0.01693,   Adjusted R-squared:  -0.0007785 
F-statistic: 0.9561 on 2 and 111 DF,  p-value: 0.3876

Call:
betareg(formula = MSE_rescaled ~ religion_cat3, data = d_sim_ch_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.4106 -0.8767  0.0854  0.8348  1.5985 

Coefficients (mean model with logit link):
                       Estimate Std. Error z value Pr(>|z|)    
(Intercept)            -0.71395    0.08789  -8.123 4.55e-16 ***
religion_cat3_buddhist -0.08271    0.12047  -0.687    0.492    
religion_cat3_other     0.18629    0.14823   1.257    0.209    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)    9.831      1.251   7.856 3.98e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 65.74 on 4 Df
Pseudo R-squared: 0.01404
Number of iterations: 10 (BFGS) + 2 (Fisher scoring) 

Target


Call:
lm(formula = scale(MSE) ~ target, data = d_sim_ch_adults, contrasts = list(target = "contr.sum"))

Residuals:
    Min      1Q  Median      3Q     Max 
-1.8115 -0.7021 -0.1410  0.8369  1.8917 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)   
(Intercept)  0.001568   0.082752   0.019  0.98491   
target1     -0.389446   0.244940  -1.590  0.11435   
target2     -0.044120   0.244940  -0.180  0.85734   
target3      0.129110   0.244940   0.527  0.59904   
target4      0.489456   0.253148   1.933  0.05542 . 
target5     -0.327869   0.244940  -1.339  0.18312   
target6      0.020732   0.244940   0.085  0.93268   
target7     -0.739605   0.253148  -2.922  0.00413 **
target8      0.179084   0.253148   0.707  0.48061   
target9      0.398358   0.244940   1.626  0.10637   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9644 on 126 degrees of freedom
Multiple R-squared:  0.1319,    Adjusted R-squared:  0.06992 
F-statistic: 2.128 on 9 and 126 DF,  p-value: 0.03173

Call:
betareg(formula = MSE_rescaled ~ target, data = d_sim_ch_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.9833 -0.7049 -0.0544  0.8892  1.9400 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.82754    0.05447 -15.192  < 2e-16 ***
target1     -0.25057    0.16347  -1.533  0.12532    
target2      0.01618    0.15710   0.103  0.91799    
target3      0.07734    0.15588   0.496  0.61979    
target4      0.34541    0.15673   2.204  0.02753 *  
target5     -0.35136    0.16632  -2.113  0.03464 *  
target6      0.03774    0.15666   0.241  0.80962    
target7     -0.47680    0.17598  -2.709  0.00674 ** 
target8      0.15236    0.15967   0.954  0.33998    
target9      0.25788    0.15287   1.687  0.09161 .  

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   10.828      1.268   8.538   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 86.48 on 11 Df
Pseudo R-squared: 0.1445
Number of iterations: 21 (BFGS) + 2 (Fisher scoring) 

All together


Call:
lm(formula = scale(MSE) ~ scale(age) + gender + scale(education_catX) + 
    religion_cat3 + urban_rural_cat2 + target, data = d_sim_ch_adults %>% 
    mutate(education_catX = as.numeric(education_catX)))

Residuals:
     Min       1Q   Median       3Q      Max 
-1.80819 -0.71922 -0.04958  0.84399  1.84406 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)  
(Intercept)             0.10191    0.14769   0.690    0.492  
scale(age)              0.02301    0.11639   0.198    0.844  
gender_m                0.10885    0.10180   1.069    0.288  
scale(education_catX)  -0.15335    0.11667  -1.314    0.192  
religion_cat3_buddhist -0.04976    0.19828  -0.251    0.802  
religion_cat3_other     0.15413    0.25251   0.610    0.543  
urban_rural_cat2_rural -0.08875    0.10681  -0.831    0.408  
target1                -0.27630    0.27711  -0.997    0.321  
target2                -0.36463    0.33601  -1.085    0.281  
target3                 0.08036    0.25961   0.310    0.758  
target4                 0.52215    0.28595   1.826    0.071 .
target5                -0.23257    0.29221  -0.796    0.428  
target6                -0.09449    0.26274  -0.360    0.720  
target7                -0.45242    0.31609  -1.431    0.156  
target8                 0.24088    0.27284   0.883    0.380  
target9                 0.29498    0.29328   1.006    0.317  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9853 on 95 degrees of freedom
  (25 observations deleted due to missingness)
Multiple R-squared:  0.1359,    Adjusted R-squared:  -0.0004857 
F-statistic: 0.9964 on 15 and 95 DF,  p-value: 0.4655

Call:
lm(formula = scale(MSE) ~ scale(age) + gender + education_cat2 + 
    religion_cat3 + urban_rural_cat2 + target, data = d_sim_ch_adults)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.8763 -0.7073 -0.1275  0.8295  1.8547 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)  
(Intercept)             0.12959    0.14389   0.901   0.3701  
scale(age)              0.01135    0.11545   0.098   0.9219  
gender_m                0.10224    0.10128   1.009   0.3153  
education_cat2_coll    -0.18022    0.11307  -1.594   0.1143  
religion_cat3_buddhist -0.05008    0.19692  -0.254   0.7998  
religion_cat3_other     0.15839    0.24911   0.636   0.5264  
urban_rural_cat2_rural -0.09263    0.10564  -0.877   0.3828  
target1                -0.27895    0.27551  -1.012   0.3139  
target2                -0.35094    0.33329  -1.053   0.2950  
target3                 0.06198    0.25926   0.239   0.8116  
target4                 0.53953    0.28512   1.892   0.0615 .
target5                -0.22798    0.29054  -0.785   0.4346  
target6                -0.07104    0.26096  -0.272   0.7860  
target7                -0.46661    0.31462  -1.483   0.1414  
target8                 0.22954    0.27000   0.850   0.3974  
target9                 0.26820    0.29235   0.917   0.3613  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9812 on 95 degrees of freedom
  (25 observations deleted due to missingness)
Multiple R-squared:  0.1431,    Adjusted R-squared:  0.007854 
F-statistic: 1.058 on 15 and 95 DF,  p-value: 0.4054
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(MSE) ~ scale(age) + gender + scale(education_catX) + religion_cat3 +  
    urban_rural_cat2 + +(1 | target)
   Data: d_sim_ch_adults %>% mutate(education_catX = as.numeric(education_catX))

REML criterion at convergence: 322.8

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.02986 -0.84997 -0.07919  0.79709  1.82275 

Random effects:
 Groups   Name        Variance Std.Dev.
 target   (Intercept) 0.001236 0.03516 
 Residual             0.976187 0.98802 
Number of obs: 111, groups:  target, 10

Fixed effects:
                         Estimate Std. Error         df t value Pr(>|t|)
(Intercept)              0.156971   0.142408  26.696548   1.102    0.280
scale(age)              -0.007222   0.113264 102.272384  -0.064    0.949
gender_m                 0.131768   0.094980  97.398767   1.387    0.169
scale(education_catX)   -0.128565   0.114095 101.871942  -1.127    0.262
religion_cat3_buddhist  -0.106878   0.195618  99.246537  -0.546    0.586
religion_cat3_other      0.260819   0.242232 103.986230   1.077    0.284
urban_rural_cat2_rural  -0.046704   0.101603 103.886042  -0.460    0.647

Correlation of Fixed Effects:
            (Intr) scl(g) gndr_m sc(_X) rlgn_ct3_b rlgn_ct3_t
scale(age)   0.067                                           
gender_m     0.033  0.117                                    
scl(dctn_X)  0.180  0.463 -0.002                             
rlgn_ct3_bd -0.105 -0.194 -0.021 -0.113                      
rlgn_ct3_th  0.547  0.151  0.019  0.181 -0.772               
urbn_rrl_2_  0.176 -0.087 -0.083  0.249 -0.110      0.158    
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(MSE) ~ scale(age) + gender + education_cat2 + religion_cat3 +  
    urban_rural_cat2 + +(1 | target)
   Data: d_sim_ch_adults

REML criterion at convergence: 322

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.0241 -0.8635 -0.0846  0.8195  1.8610 

Random effects:
 Groups   Name        Variance Std.Dev.
 target   (Intercept) 0.001573 0.03966 
 Residual             0.968531 0.98414 
Number of obs: 111, groups:  target, 10

Fixed effects:
                        Estimate Std. Error        df t value Pr(>|t|)
(Intercept)              0.17841    0.13976  26.03653   1.277    0.213
scale(age)              -0.01944    0.11166 103.17213  -0.174    0.862
gender_m                 0.12745    0.09468  97.79777   1.346    0.181
education_cat2_coll     -0.16033    0.11144 100.32917  -1.439    0.153
religion_cat3_buddhist  -0.10567    0.19445  99.01632  -0.543    0.588
religion_cat3_other      0.26022    0.23983 103.88604   1.085    0.280
urban_rural_cat2_rural  -0.05277    0.10092 103.99192  -0.523    0.602

Correlation of Fixed Effects:
            (Intr) scl(g) gndr_m edc_2_ rlgn_ct3_b rlgn_ct3_t
scale(age)  -0.001                                           
gender_m     0.035  0.132                                    
edctn_ct2_c  0.037  0.445  0.030                             
rlgn_ct3_bd -0.089 -0.184 -0.024 -0.093                      
rlgn_ct3_th  0.530  0.133  0.024  0.144 -0.772               
urbn_rrl_2_  0.142 -0.099 -0.076  0.238 -0.104      0.149    

Call:
betareg(formula = MSE_rescaled ~ scale(age) + gender + scale(education_catX) + religion_cat3 + 
    urban_rural_cat2 + target, data = d_sim_ch_adults %>% mutate(education_catX = as.numeric(education_catX)))

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.2342 -0.7313  0.0402  0.9603  1.9925 

Coefficients (mean model with logit link):
                       Estimate Std. Error z value Pr(>|z|)    
(Intercept)            -0.76244    0.08999  -8.473   <2e-16 ***
scale(age)              0.03992    0.07115   0.561   0.5748    
gender_m                0.06672    0.06240   1.069   0.2849    
scale(education_catX)  -0.09461    0.07193  -1.315   0.1884    
religion_cat3_buddhist -0.02384    0.12011  -0.199   0.8427    
religion_cat3_other     0.08275    0.15112   0.548   0.5840    
urban_rural_cat2_rural -0.05144    0.06600  -0.779   0.4358    
target1                -0.18546    0.17421  -1.065   0.2871    
target2                -0.20030    0.20975  -0.955   0.3396    
target3                 0.04251    0.15848   0.268   0.7885    
target4                 0.35641    0.16940   2.104   0.0354 *  
target5                -0.25484    0.18546  -1.374   0.1694    
target6                -0.03240    0.16074  -0.202   0.8403    
target7                -0.25495    0.20345  -1.253   0.2102    
target8                 0.17183    0.16549   1.038   0.2991    
target9                 0.18358    0.17598   1.043   0.2969    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   11.398      1.479   7.709 1.27e-14 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 71.79 on 17 Df
Pseudo R-squared: 0.1356
Number of iterations: 26 (BFGS) + 2 (Fisher scoring) 

Call:
betareg(formula = MSE_rescaled ~ scale(age) + gender + education_cat2 + religion_cat3 + 
    urban_rural_cat2 + target, data = d_sim_ch_adults %>% mutate(education_catX = as.numeric(education_catX)))

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.2420 -0.7853  0.0128  0.9615  1.9850 

Coefficients (mean model with logit link):
                       Estimate Std. Error z value Pr(>|z|)    
(Intercept)            -0.74582    0.08748  -8.526   <2e-16 ***
scale(age)              0.03074    0.07061   0.435   0.6633    
gender_m                0.06228    0.06211   1.003   0.3160    
education_cat2_coll    -0.11724    0.06958  -1.685   0.0920 .  
religion_cat3_buddhist -0.02138    0.11922  -0.179   0.8577    
religion_cat3_other     0.08214    0.14904   0.551   0.5815    
urban_rural_cat2_rural -0.05626    0.06533  -0.861   0.3891    
target1                -0.18743    0.17317  -1.082   0.2791    
target2                -0.19209    0.20794  -0.924   0.3556    
target3                 0.02920    0.15839   0.184   0.8537    
target4                 0.36565    0.16895   2.164   0.0304 *  
target5                -0.24980    0.18436  -1.355   0.1754    
target6                -0.02218    0.15974  -0.139   0.8896    
target7                -0.26495    0.20257  -1.308   0.1909    
target8                 0.16606    0.16376   1.014   0.3106    
target9                 0.16899    0.17536   0.964   0.3352    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   11.516      1.494   7.706  1.3e-14 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 72.37 on 17 Df
Pseudo R-squared: 0.1453
Number of iterations: 26 (BFGS) + 2 (Fisher scoring) 

China children

Age


Call:
lm(formula = scale(MSE) ~ scale(age), data = d_sim_ch_children)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7635 -0.7910 -0.1152  0.7655  2.5044 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0007363  0.0876920   0.008    0.993
scale(age)  0.0902228  0.0880741   1.024    0.308

Residual standard error: 0.9998 on 128 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.008132,  Adjusted R-squared:  0.0003827 
F-statistic: 1.049 on 1 and 128 DF,  p-value: 0.3076

Call:
betareg(formula = MSE_rescaled ~ scale(age), data = d_sim_ch_children)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.0806 -0.6844  0.0900  0.8033  1.9425 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.18297    0.06077 -19.465   <2e-16 ***
scale(age)   0.05430    0.05864   0.926    0.354    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   10.545      1.274   8.279   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 94.81 on 3 Df
Pseudo R-squared: 0.007183
Number of iterations: 13 (BFGS) + 1 (Fisher scoring) 

Gender


Call:
lm(formula = scale(MSE) ~ gender, data = d_sim_ch_children)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.63203 -0.85381 -0.08124  0.71790  2.46064 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0005309  0.0880053  -0.006    0.995
gender_m    -0.0345062  0.0880053  -0.392    0.696

Residual standard error: 1.003 on 128 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.0012,    Adjusted R-squared:  -0.006604 
F-statistic: 0.1537 on 1 and 128 DF,  p-value: 0.6956

Call:
betareg(formula = MSE_rescaled ~ gender, data = d_sim_ch_children)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.8828 -0.7265  0.1241  0.7659  1.9251 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.18303    0.06095 -19.411   <2e-16 ***
gender_m    -0.01553    0.05860  -0.265    0.791    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   10.478      1.265   8.281   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood:  94.4 on 3 Df
Pseudo R-squared: 0.0005475
Number of iterations: 12 (BFGS) + 2 (Fisher scoring) 

Target


Call:
lm(formula = scale(MSE) ~ target, data = d_sim_ch_children, contrasts = list(target = "contr.sum"))

Residuals:
     Min       1Q   Median       3Q      Max 
-1.94725 -0.55250 -0.07091  0.60449  2.59776 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)  0.05087    0.11494   0.443  0.65887   
target1     -0.02600    0.26410  -0.098  0.92173   
target2      0.15149    0.25634   0.591  0.55566   
target3      0.58204    0.86498   0.673  0.50232   
target4     -0.20095    0.28289  -0.710  0.47888   
target5     -0.06192    0.25634  -0.242  0.80953   
target6      0.37729    0.26410   1.429  0.15575   
target7     -0.50273    0.27287  -1.842  0.06791 . 
target8     -0.80103    0.26410  -3.033  0.00297 **
target9      0.10309    0.25634   0.402  0.68830   
target10    -0.34742    0.26410  -1.315  0.19087   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9478 on 119 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.1713,    Adjusted R-squared:  0.1017 
F-statistic:  2.46 on 10 and 119 DF,  p-value: 0.0104

Call:
betareg(formula = MSE_rescaled ~ target, data = d_sim_ch_children)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.0700 -0.5880  0.0966  0.7542  2.2198 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.16378    0.07347 -15.840  < 2e-16 ***
target1     -0.02523    0.17098  -0.148  0.88269    
target2      0.15964    0.16118   0.990  0.32194    
target3      0.44977    0.51959   0.866  0.38670    
target4     -0.15569    0.18768  -0.830  0.40678    
target5     -0.02180    0.16579  -0.131  0.89540    
target6      0.17170    0.16577   1.036  0.30031    
target7     -0.33839    0.18735  -1.806  0.07089 .  
target8     -0.59175    0.19075  -3.102  0.00192 ** 
target9      0.11634    0.16221   0.717  0.47324    
target10    -0.21760    0.17695  -1.230  0.21880    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   12.649      1.536   8.235   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 106.3 on 12 Df
Pseudo R-squared: 0.1726
Number of iterations: 21 (BFGS) + 2 (Fisher scoring) 

All together


Call:
lm(formula = scale(MSE) ~ scale(age) + gender + target, data = d_sim_ch_children)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.78651 -0.59622 -0.05927  0.52989  2.55854 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)   
(Intercept)  0.037040   0.115840   0.320  0.74972   
scale(age)   0.064963   0.088003   0.738  0.46187   
gender_m    -0.087524   0.086622  -1.010  0.31438   
target1      0.008039   0.266616   0.030  0.97600   
target2      0.134658   0.259306   0.519  0.60453   
target3      0.439427   0.876055   0.502  0.61689   
target4     -0.141745   0.288276  -0.492  0.62385   
target5     -0.015492   0.260035  -0.060  0.95260   
target6      0.381900   0.265512   1.438  0.15300   
target7     -0.529608   0.274554  -1.929  0.05616 . 
target8     -0.805686   0.265134  -3.039  0.00293 **
target9      0.147945   0.260197   0.569  0.57072   
target10    -0.347887   0.267575  -1.300  0.19611   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9502 on 117 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.181, Adjusted R-squared:  0.09704 
F-statistic: 2.155 on 12 and 117 DF,  p-value: 0.01829
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(MSE) ~ scale(age) + gender + +(1 | target)
   Data: d_sim_ch_children

REML criterion at convergence: 370.4

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.6938 -0.7400 -0.1060  0.6657  2.5110 

Random effects:
 Groups   Name        Variance Std.Dev.
 target   (Intercept) 0.1183   0.3439  
 Residual             0.8989   0.9481  
Number of obs: 130, groups:  target, 11

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept)   0.003062   0.135961   9.250871   0.023    0.983
scale(age)    0.079035   0.086095 123.704921   0.918    0.360
gender_m     -0.073540   0.085141 122.345763  -0.864    0.389

Correlation of Fixed Effects:
           (Intr) scl(g)
scale(age) -0.003       
gender_m    0.017 -0.122

Vanuatu

Vanuatu adults

Age


Call:
lm(formula = scale(MSE) ~ scale(age), data = d_sim_vt_adults)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.3978 -1.0958 -0.1866  0.9466  1.6399 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.005177   0.084151  -0.062    0.951
scale(age)   0.004487   0.084449   0.053    0.958

Residual standard error: 1.003 on 140 degrees of freedom
  (6 observations deleted due to missingness)
Multiple R-squared:  2.017e-05, Adjusted R-squared:  -0.007123 
F-statistic: 0.002824 on 1 and 140 DF,  p-value: 0.9577

Call:
betareg(formula = MSE_rescaled ~ scale(age), data = d_sim_vt_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.5191 -1.1249  0.1113  0.8946  1.3277 

Coefficients (mean model with logit link):
             Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.101665   0.075570  -14.58   <2e-16 ***
scale(age)  -0.007842   0.071174   -0.11    0.912    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   5.4708     0.6208   8.812   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 69.24 on 3 Df
Pseudo R-squared: 0.000101
Number of iterations: 13 (BFGS) + 1 (Fisher scoring) 

Gender


Call:
lm(formula = scale(MSE) ~ gender, data = d_sim_vt_adults)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.4115 -1.0972 -0.1551  0.9793  1.6432 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)  0.01622    0.08903   0.182    0.856
gender_m     0.04287    0.08903   0.482    0.631

Residual standard error: 1.003 on 146 degrees of freedom
Multiple R-squared:  0.001586,  Adjusted R-squared:  -0.005253 
F-statistic: 0.2319 on 1 and 146 DF,  p-value: 0.6309

Call:
betareg(formula = MSE_rescaled ~ gender, data = d_sim_vt_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.4878 -1.1435  0.1294  0.9184  1.3132 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.08823    0.07900 -13.774   <2e-16 ***
gender_m     0.02521    0.07481   0.337    0.736    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   5.4614     0.6069   8.999   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 71.84 on 3 Df
Pseudo R-squared: 0.0007893
Number of iterations: 13 (BFGS) + 2 (Fisher scoring) 

Location


Call:
lm(formula = scale(MSE) ~ location_cat2, data = d_sim_vt_adults)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.57886 -0.92190 -0.09571  0.96541  1.83714 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)   
(Intercept)         -2.053e-17  8.032e-02   0.000  1.00000   
location_cat2_urban  2.264e-01  8.032e-02   2.819  0.00549 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9772 on 146 degrees of freedom
Multiple R-squared:  0.05162,   Adjusted R-squared:  0.04512 
F-statistic: 7.946 on 1 and 146 DF,  p-value: 0.005488

Call:
betareg(formula = MSE_rescaled ~ location_cat2, data = d_sim_vt_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.4571 -1.0025  0.1772  0.9292  1.4307 

Coefficients (mean model with logit link):
                    Estimate Std. Error z value Pr(>|z|)    
(Intercept)         -1.10317    0.07311 -15.089   <2e-16 ***
location_cat2_urban  0.16126    0.06878   2.345   0.0191 *  

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   5.6687     0.6314   8.978   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 74.49 on 3 Df
Pseudo R-squared: 0.03722
Number of iterations: 14 (BFGS) + 2 (Fisher scoring) 

Target


Call:
lm(formula = scale(MSE) ~ target, data = d_sim_vt_adults, contrasts = list(target = "contr.sum"))

Residuals:
    Min      1Q  Median      3Q     Max 
-2.1363 -0.6290 -0.1473  0.6257  2.0004 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.03428    0.08934   0.384   0.7018    
target1     -0.43628    0.22886  -1.906   0.0587 .  
target2      0.18645    0.22886   0.815   0.4167    
target3     -0.03812    0.24333  -0.157   0.8758    
target4      0.35781    0.58392   0.613   0.5410    
target5      0.19330    0.23569   0.820   0.4136    
target6     -0.15988    0.22886  -0.699   0.4860    
target7     -0.29295    0.22886  -1.280   0.2027    
target8     -0.99274    0.22886  -4.338 2.77e-05 ***
target9      0.30521    0.23569   1.295   0.1975    
target10    -0.07647    0.22886  -0.334   0.7388    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9022 on 137 degrees of freedom
Multiple R-squared:  0.2414,    Adjusted R-squared:  0.186 
F-statistic:  4.36 on 10 and 137 DF,  p-value: 2.616e-05

Call:
betareg(formula = MSE_rescaled ~ target, data = d_sim_vt_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.9923 -0.5070  0.1645  0.7768  1.7255 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.09169    0.07850 -13.906  < 2e-16 ***
target1     -0.38694    0.20656  -1.873 0.061030 .  
target2      0.08989    0.19358   0.464 0.642375    
target3     -0.04871    0.20950  -0.233 0.816143    
target4      0.43429    0.47368   0.917 0.359216    
target5      0.12096    0.19862   0.609 0.542509    
target6     -0.19922    0.20103  -0.991 0.321683    
target7     -0.15220    0.19972  -0.762 0.446023    
target8     -0.83086    0.22069  -3.765 0.000167 ***
target9      0.26711    0.19544   1.367 0.171702    
target10    -0.04615    0.19690  -0.234 0.814684    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   6.9512     0.7828    8.88   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 89.01 on 12 Df
Pseudo R-squared: 0.2161
Number of iterations: 22 (BFGS) + 2 (Fisher scoring) 

All together


Call:
lm(formula = scale(MSE) ~ scale(age) + gender + location_cat2 + 
    target, data = d_sim_vt_adults)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.30230 -0.60303 -0.08047  0.59366  1.94575 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)          0.076090   0.095069   0.800  0.42498    
scale(age)          -0.009992   0.075740  -0.132  0.89526    
gender_m             0.069748   0.082202   0.848  0.39774    
location_cat2_urban  0.208243   0.075175   2.770  0.00644 ** 
target1             -0.486842   0.226481  -2.150  0.03347 *  
target2              0.223195   0.232775   0.959  0.33944    
target3             -0.059356   0.239496  -0.248  0.80466    
target4              0.604214   0.582132   1.038  0.30126    
target5              0.170536   0.231806   0.736  0.46327    
target6             -0.312368   0.238869  -1.308  0.19332    
target7             -0.270313   0.226255  -1.195  0.23440    
target8             -0.973820   0.232840  -4.182 5.31e-05 ***
target9              0.314015   0.231381   1.357  0.17713    
target10            -0.141242   0.234133  -0.603  0.54740    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8832 on 128 degrees of freedom
  (6 observations deleted due to missingness)
Multiple R-squared:  0.2908,    Adjusted R-squared:  0.2187 
F-statistic: 4.036 on 13 and 128 DF,  p-value: 1.542e-05
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(MSE) ~ scale(age) + gender + location_cat2 + +(1 | target)
   Data: d_sim_vt_adults

REML criterion at convergence: 391.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3711 -0.7719 -0.1157  0.7591  2.1208 

Random effects:
 Groups   Name        Variance Std.Dev.
 target   (Intercept) 0.2061   0.4540  
 Residual             0.7789   0.8826  
Number of obs: 142, groups:  target, 11

Fixed effects:
                      Estimate Std. Error         df t value Pr(>|t|)   
(Intercept)          3.893e-02  1.613e-01  1.046e+01   0.241  0.81394   
scale(age)          -2.487e-04  7.522e-02  1.302e+02  -0.003  0.99737   
gender_m             5.942e-02  8.140e-02  1.321e+02   0.730  0.46668   
location_cat2_urban  2.055e-01  7.476e-02  1.299e+02   2.750  0.00682 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) scl(g) gndr_m
scale(age)  -0.024              
gender_m     0.188 -0.065       
lctn_ct2_rb  0.013  0.067 -0.032

Call:
betareg(formula = MSE_rescaled ~ scale(age) + gender + location_cat2 + target, data = d_sim_vt_adults)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.5871 -0.5685  0.1536  0.7918  1.7780 

Coefficients (mean model with logit link):
                    Estimate Std. Error z value Pr(>|z|)    
(Intercept)         -1.06328    0.08271 -12.855  < 2e-16 ***
scale(age)          -0.01158    0.06568  -0.176 0.860019    
gender_m             0.06627    0.07087   0.935 0.349699    
location_cat2_urban  0.16303    0.06526   2.498 0.012484 *  
target1             -0.46056    0.20559  -2.240 0.025078 *  
target2              0.14424    0.19473   0.741 0.458857    
target3             -0.06243    0.20630  -0.303 0.762164    
target4              0.63952    0.47304   1.352 0.176396    
target5              0.11098    0.19521   0.569 0.569691    
target6             -0.32783    0.21468  -1.527 0.126748    
target7             -0.13385    0.19777  -0.677 0.498523    
target8             -0.83928    0.22575  -3.718 0.000201 ***
target9              0.29020    0.19160   1.515 0.129885    
target10            -0.09398    0.20161  -0.466 0.641100    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   7.3748     0.8505   8.671   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 89.52 on 15 Df
Pseudo R-squared: 0.2536
Number of iterations: 24 (BFGS) + 2 (Fisher scoring) 

Vanuatu children

Age


Call:
lm(formula = scale(MSE) ~ scale(age), data = d_sim_vt_children)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.8608 -0.9267  0.1151  0.8844  1.4462 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)  0.01157    0.08368   0.138    0.890
scale(age)   0.02228    0.08398   0.265    0.791

Residual standard error: 0.9972 on 140 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.0005025, Adjusted R-squared:  -0.006637 
F-statistic: 0.07038 on 1 and 140 DF,  p-value: 0.7912

Call:
betareg(formula = MSE_rescaled ~ scale(age), data = d_sim_vt_children)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.1805 -0.8564  0.1844  0.8695  1.3754 

Coefficients (mean model with logit link):
             Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.430248   0.048815  -8.814   <2e-16 ***
scale(age)   0.006664   0.048773   0.137    0.891    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)    11.32       1.29   8.771   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 80.22 on 3 Df
Pseudo R-squared: 0.0001346
Number of iterations: 9 (BFGS) + 2 (Fisher scoring) 

Gender


Call:
lm(formula = scale(MSE) ~ gender, data = d_sim_vt_children)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.86647 -0.90478  0.09592  0.88104  1.46081 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.008161   0.084711  -0.096    0.923
gender_m    -0.055574   0.084711  -0.656    0.513

Residual standard error: 1.002 on 141 degrees of freedom
Multiple R-squared:  0.003043,  Adjusted R-squared:  -0.004028 
F-statistic: 0.4304 on 1 and 141 DF,  p-value: 0.5129

Call:
betareg(formula = MSE_rescaled ~ gender, data = d_sim_vt_children)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.1830 -0.8390  0.1678  0.8723  1.3920 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.44208    0.04950  -8.931   <2e-16 ***
gender_m    -0.03318    0.04928  -0.673    0.501    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   11.198      1.272   8.805   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 80.26 on 3 Df
Pseudo R-squared: 0.003177
Number of iterations: 11 (BFGS) + 2 (Fisher scoring) 

Target


Call:
lm(formula = scale(MSE) ~ target, data = d_sim_vt_children, contrasts = list(target = "contr.sum"))

Residuals:
     Min       1Q   Median       3Q      Max 
-2.22143 -0.59576  0.05297  0.63392  2.01145 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.02832    0.07872   0.360  0.71964    
target1     -0.67395    0.23952  -2.814  0.00565 ** 
target2     -0.51665    0.23176  -2.229  0.02749 *  
target3      0.29425    0.29892   0.984  0.32672    
target4      0.38686    0.34215   1.131  0.26025    
target5      0.23323    0.22483   1.037  0.30145    
target6      0.36705    0.23176   1.584  0.11565    
target7      0.55082    0.21857   2.520  0.01293 *  
target8     -0.95911    0.22483  -4.266 3.77e-05 ***
target9     -0.13908    0.22483  -0.619  0.53723    
target10     0.09963    0.23952   0.416  0.67810    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9017 on 132 degrees of freedom
Multiple R-squared:  0.2442,    Adjusted R-squared:  0.1869 
F-statistic: 4.265 on 10 and 132 DF,  p-value: 3.732e-05

Call:
betareg(formula = MSE_rescaled ~ target, data = d_sim_vt_children)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.0861 -0.6609  0.1370  0.7399  2.2219 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.42754    0.04514  -9.472  < 2e-16 ***
target1     -0.42361    0.14315  -2.959  0.00309 ** 
target2     -0.31615    0.13636  -2.318  0.02043 *  
target3      0.19121    0.16786   1.139  0.25467    
target4      0.24399    0.19166   1.273  0.20300    
target5      0.13825    0.12671   1.091  0.27527    
target6      0.21419    0.13015   1.646  0.09981 .  
target7      0.31819    0.12241   2.599  0.00934 ** 
target8     -0.56957    0.13749  -4.143 3.43e-05 ***
target9     -0.06163    0.12854  -0.479  0.63159    
target10     0.05224    0.13569   0.385  0.70023    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   14.787      1.696   8.716   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood:   100 on 12 Df
Pseudo R-squared: 0.2479
Number of iterations: 21 (BFGS) + 2 (Fisher scoring) 

All together


Call:
lm(formula = scale(MSE) ~ scale(age) + gender + target, data = d_sim_vt_children)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.23284 -0.57970  0.03138  0.58494  1.98058 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.03605    0.07853   0.459  0.64698    
scale(age)  -0.00728    0.07643  -0.095  0.92427    
gender_m    -0.04021    0.07775  -0.517  0.60588    
target1     -0.68118    0.23828  -2.859  0.00496 ** 
target2     -0.51908    0.22956  -2.261  0.02542 *  
target3      0.27485    0.29517   0.931  0.35352    
target4      0.38024    0.33757   1.126  0.26209    
target5      0.22451    0.22239   1.010  0.31461    
target6      0.34737    0.22874   1.519  0.13130    
target7      0.67889    0.22244   3.052  0.00276 ** 
target8     -0.98538    0.22306  -4.418 2.09e-05 ***
target9     -0.16564    0.22303  -0.743  0.45903    
target10     0.10364    0.23916   0.433  0.66548    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8885 on 129 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.2688,    Adjusted R-squared:  0.2008 
F-statistic: 3.953 on 12 and 129 DF,  p-value: 3.458e-05
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(MSE) ~ scale(age) + gender + +(1 | target)
   Data: d_sim_vt_children

REML criterion at convergence: 391.1

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.44409 -0.81803  0.08568  0.71906  2.05346 

Random effects:
 Groups   Name        Variance Std.Dev.
 target   (Intercept) 0.2182   0.4672  
 Residual             0.7880   0.8877  
Number of obs: 142, groups:  target, 11

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept)   0.025394   0.160746  10.417215   0.158    0.877
scale(age)   -0.000469   0.076012 131.630258  -0.006    0.995
gender_m     -0.041024   0.077200 132.360377  -0.531    0.596

Correlation of Fixed Effects:
           (Intr) scl(g)
scale(age) -0.001       
gender_m    0.070 -0.035

Call:
betareg(formula = MSE_rescaled ~ scale(age) + gender + target, data = d_sim_vt_children)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.2043 -0.6984  0.0942  0.7788  2.3123 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.42257    0.04466  -9.463  < 2e-16 ***
scale(age)  -0.00811    0.04345  -0.187  0.85194    
gender_m    -0.01615    0.04421  -0.365  0.71491    
target1     -0.43278    0.14123  -3.064  0.00218 ** 
target2     -0.32141    0.13397  -2.399  0.01644 *  
target3      0.17955    0.16438   1.092  0.27470    
target4      0.23957    0.18750   1.278  0.20134    
target5      0.13341    0.12430   1.073  0.28316    
target6      0.20318    0.12737   1.595  0.11068    
target7      0.40402    0.12340   3.274  0.00106 ** 
target8     -0.58371    0.13530  -4.314  1.6e-05 ***
target9     -0.07637    0.12648  -0.604  0.54598    
target10     0.05178    0.13439   0.385  0.70005    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   15.545      1.792   8.674   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 102.6 on 14 Df
Pseudo R-squared: 0.2747
Number of iterations: 22 (BFGS) + 2 (Fisher scoring) 

Using adults’ models for individual children

US

Age


Call:
lm(formula = scale(MSE) ~ scale(age), data = d_sim_us_adch)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.5302 -0.7290 -0.2359  0.7080  2.2975 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)  0.02368    0.09131   0.259  0.79584   
scale(age)  -0.24796    0.09173  -2.703  0.00797 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9621 on 109 degrees of freedom
  (6 observations deleted due to missingness)
Multiple R-squared:  0.06282,   Adjusted R-squared:  0.05423 
F-statistic: 7.307 on 1 and 109 DF,  p-value: 0.00797

Call:
betareg(formula = MSE_rescaled ~ scale(age), data = d_sim_us_adch)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.9037 -0.6458 -0.0220  0.8135  1.8915 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.07931    0.06948 -15.535  < 2e-16 ***
scale(age)  -0.19634    0.06735  -2.915  0.00355 ** 

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)    8.858      1.150   7.702 1.34e-14 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 70.28 on 3 Df
Pseudo R-squared: 0.07682
Number of iterations: 12 (BFGS) + 2 (Fisher scoring) 

Gender


Call:
lm(formula = scale(MSE) ~ gender, data = d_sim_us_adch)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.59916 -0.83868 -0.09822  0.64239  2.15513 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.01470    0.09337  -0.157    0.875
gender_m    -0.10117    0.09337  -1.083    0.281

Residual standard error: 0.9993 on 115 degrees of freedom
Multiple R-squared:  0.0101,    Adjusted R-squared:  0.001497 
F-statistic: 1.174 on 1 and 115 DF,  p-value: 0.2809

Call:
betareg(formula = MSE_rescaled ~ gender, data = d_sim_us_adch)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.8536 -0.7803  0.1046  0.7064  1.7097 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.09860    0.07147  -15.37   <2e-16 ***
gender_m    -0.05957    0.06850   -0.87    0.384    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)    8.048      1.015   7.929  2.2e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 69.79 on 3 Df
Pseudo R-squared: 0.006656
Number of iterations: 12 (BFGS) + 2 (Fisher scoring) 

Race/ethnicity


Call:
lm(formula = scale(MSE) ~ ethnicity_cat2, data = d_sim_us_adch)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.73204 -0.74203 -0.05494  0.70526  2.21985 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)
(Intercept)        -0.01171    0.10684  -0.110    0.913
ethnicity_cat2_POC -0.15324    0.10684  -1.434    0.155

Residual standard error: 0.9608 on 90 degrees of freedom
  (25 observations deleted due to missingness)
Multiple R-squared:  0.02235,   Adjusted R-squared:  0.01148 
F-statistic: 2.057 on 1 and 90 DF,  p-value: 0.155

Call:
betareg(formula = MSE_rescaled ~ ethnicity_cat2, data = d_sim_us_adch)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.2025 -0.6368  0.1387  0.7532  1.7655 

Coefficients (mean model with logit link):
                   Estimate Std. Error z value Pr(>|z|)    
(Intercept)        -1.11860    0.08348 -13.400   <2e-16 ***
ethnicity_cat2_POC -0.06768    0.08028  -0.843    0.399    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)    8.325      1.188   7.009  2.4e-12 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 57.84 on 3 Df
Pseudo R-squared: 0.007623
Number of iterations: 8 (BFGS) + 2 (Fisher scoring) 

Religion


Call:
lm(formula = scale(MSE) ~ religion_cat3, data = d_sim_us_adch)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.6670 -0.7996 -0.1913  0.6339  1.9324 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)  
(Intercept)             -0.07364    0.10419  -0.707   0.4813  
religion_cat3_christian  0.15016    0.13389   1.122   0.2648  
religion_cat3_other     -0.34635    0.16113  -2.150   0.0341 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9985 on 98 degrees of freedom
  (16 observations deleted due to missingness)
Multiple R-squared:  0.04503,   Adjusted R-squared:  0.02554 
F-statistic:  2.31 on 2 and 98 DF,  p-value: 0.1046

Call:
betareg(formula = MSE_rescaled ~ religion_cat3, data = d_sim_us_adch)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.1849 -0.7007  0.0240  0.7135  1.6105 

Coefficients (mean model with logit link):
                        Estimate Std. Error z value Pr(>|z|)    
(Intercept)             -1.14672    0.08038 -14.266   <2e-16 ***
religion_cat3_christian  0.10450    0.09796   1.067   0.2861    
religion_cat3_other     -0.25667    0.12180  -2.107   0.0351 *  

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)    8.250      1.122   7.353 1.94e-13 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 62.15 on 4 Df
Pseudo R-squared: 0.04868
Number of iterations: 11 (BFGS) + 2 (Fisher scoring) 

Target


Call:
lm(formula = scale(MSE) ~ target, data = d_sim_us_adch, contrasts = list(target = "contr.sum"))

Residuals:
    Min      1Q  Median      3Q     Max 
-1.5739 -0.6128 -0.1199  0.5806  2.3814 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.021141   0.076516  -0.276 0.782854    
target1     -0.565637   0.235312  -2.404 0.017946 *  
target2      0.142811   0.226374   0.631 0.529477    
target3     -0.003955   0.218526  -0.018 0.985595    
target4     -0.189887   0.226374  -0.839 0.403441    
target5     -0.265973   0.218526  -1.217 0.226237    
target6     -0.373798   0.235312  -1.589 0.115120    
target7     -0.950286   0.245608  -3.869 0.000188 ***
target8      0.096565   0.235312   0.410 0.682354    
target9      0.884649   0.226374   3.908 0.000164 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8251 on 107 degrees of freedom
Multiple R-squared:  0.372, Adjusted R-squared:  0.3191 
F-statistic: 7.041 on 9 and 107 DF,  p-value: 6.066e-08

Call:
betareg(formula = MSE_rescaled ~ target, data = d_sim_us_adch)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.9222 -0.6841  0.0495  0.7834  2.4545 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.15756    0.06031 -19.192  < 2e-16 ***
target1     -0.40022    0.18988  -2.108 0.035054 *  
target2      0.19694    0.16334   1.206 0.227934    
target3     -0.01581    0.16338  -0.097 0.922903    
target4     -0.13562    0.17302  -0.784 0.433124    
target5     -0.12492    0.16668  -0.750 0.453551    
target6     -0.36946    0.18864  -1.959 0.050163 .  
target7     -0.78015    0.21595  -3.613 0.000303 ***
target8      0.08559    0.17277   0.495 0.620326    
target9      0.66397    0.15461   4.294 1.75e-05 ***

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   12.689      1.625   7.809 5.77e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 95.65 on 11 Df
Pseudo R-squared: 0.3533
Number of iterations: 19 (BFGS) + 2 (Fisher scoring) 

All together


Call:
lm(formula = scale(MSE) ~ scale(age) + gender + ethnicity_cat2 + 
    religion_cat3 + target, data = d_sim_us_adch)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.1612 -0.5556 -0.1462  0.4263  2.2624 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)             -0.0003225  0.1168310  -0.003 0.997806    
scale(age)              -0.2151615  0.0927814  -2.319 0.023704 *  
gender_m                 0.0252135  0.1058055   0.238 0.812434    
ethnicity_cat2_POC      -0.1467148  0.1161566  -1.263 0.211291    
religion_cat3_christian  0.0101795  0.1437128   0.071 0.943759    
religion_cat3_other     -0.0546606  0.1817050  -0.301 0.764558    
target1                 -0.5212159  0.3018113  -1.727 0.089154 .  
target2                  0.0397639  0.2764660   0.144 0.886102    
target3                  0.3351109  0.2545001   1.317 0.192772    
target4                  0.1277681  0.3191201   0.400 0.690256    
target5                 -0.2934084  0.2942442  -0.997 0.322563    
target6                 -0.5507955  0.3071590  -1.793 0.077820 .  
target7                 -0.8386141  0.3146453  -2.665 0.009795 ** 
target8                 -0.0657910  0.2915482  -0.226 0.822207    
target9                  1.0654590  0.2648380   4.023 0.000159 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8124 on 62 degrees of freedom
  (40 observations deleted due to missingness)
Multiple R-squared:  0.4394,    Adjusted R-squared:  0.3128 
F-statistic: 3.471 on 14 and 62 DF,  p-value: 0.00035
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(MSE) ~ scale(age) + gender + ethnicity_cat2 + religion_cat3 +      +(1 | target)
   Data: d_sim_us_adch

REML criterion at convergence: 208.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.4287 -0.5873 -0.1834  0.4568  2.5660 

Random effects:
 Groups   Name        Variance Std.Dev.
 target   (Intercept) 0.2476   0.4976  
 Residual             0.6603   0.8126  
Number of obs: 77, groups:  target, 10

Fixed effects:
                         Estimate Std. Error        df t value Pr(>|t|)  
(Intercept)             -0.005392   0.195523 10.964523  -0.028    0.978  
scale(age)              -0.228717   0.091553 65.011447  -2.498    0.015 *
gender_m                -0.011599   0.102618 68.288867  -0.113    0.910  
ethnicity_cat2_POC      -0.159959   0.112348 68.634615  -1.424    0.159  
religion_cat3_christian  0.009530   0.139824 67.870228   0.068    0.946  
religion_cat3_other     -0.109178   0.177292 67.345279  -0.616    0.540  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) scl(g) gndr_m e_2_PO rlgn_ct3_c
scale(age)  -0.049                                
gender_m     0.042 -0.089                         
ethnc_2_POC -0.259  0.028  0.170                  
rlgn_ct3_ch -0.198  0.031 -0.124  0.068           
rlgn_ct3_th  0.244  0.002  0.024 -0.117 -0.609    

Call:
betareg(formula = MSE_rescaled ~ scale(age) + gender + ethnicity_cat2 + religion_cat3 + 
    target, data = d_sim_us_adch)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.9154 -0.7664 -0.0799  0.7831  2.7043 

Coefficients (mean model with logit link):
                         Estimate Std. Error z value Pr(>|z|)    
(Intercept)             -1.170578   0.085653 -13.667  < 2e-16 ***
scale(age)              -0.197720   0.065407  -3.023  0.00250 ** 
gender_m                -0.003992   0.076601  -0.052  0.95844    
ethnicity_cat2_POC      -0.107063   0.080914  -1.323  0.18578    
religion_cat3_christian  0.028474   0.103690   0.275  0.78362    
religion_cat3_other     -0.072890   0.134131  -0.543  0.58684    
target1                 -0.424284   0.232128  -1.828  0.06758 .  
target2                  0.105307   0.192853   0.546  0.58503    
target3                  0.265353   0.173784   1.527  0.12678    
target4                  0.155570   0.225445   0.690  0.49016    
target5                 -0.161728   0.216744  -0.746  0.45556    
target6                 -0.518757   0.232888  -2.228  0.02591 *  
target7                 -0.732025   0.267842  -2.733  0.00628 ** 
target8                 -0.026639   0.209091  -0.127  0.89862    
target9                  0.789917   0.171210   4.614 3.96e-06 ***

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   15.155      2.405   6.302 2.93e-10 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 70.54 on 16 Df
Pseudo R-squared: 0.4389
Number of iterations: 25 (BFGS) + 1 (Fisher scoring) 

Ghana

Age


Call:
lm(formula = scale(MSE) ~ scale(age), data = d_sim_gh_adch)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.5741 -0.9666  0.1471  0.9985  1.4829 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.665e-16  8.175e-02   0.000    1.000
scale(age)  6.578e-02  8.202e-02   0.802    0.424

Residual standard error: 1.001 on 148 degrees of freedom
Multiple R-squared:  0.004326,  Adjusted R-squared:  -0.002401 
F-statistic: 0.6431 on 1 and 148 DF,  p-value: 0.4239

Call:
betareg(formula = MSE_rescaled ~ scale(age), data = d_sim_gh_adch)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.0877 -0.7355  0.3249  0.9140  1.2440 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.89197    0.07099 -12.565   <2e-16 ***
scale(age)   0.06120    0.06836   0.895    0.371    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   5.2985     0.5764   9.193   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 58.44 on 3 Df
Pseudo R-squared: 0.005533
Number of iterations: 15 (BFGS) + 2 (Fisher scoring) 

Gender


Call:
lm(formula = scale(MSE) ~ gender, data = d_sim_gh_adch)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.4895 -0.9795  0.1744  1.0006  1.4029 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0006425  0.0820358  -0.008    0.994
gender_m    -0.0120460  0.0820358  -0.147    0.883

Residual standard error: 1.003 on 148 degrees of freedom
Multiple R-squared:  0.0001457, Adjusted R-squared:  -0.00661 
F-statistic: 0.02156 on 1 and 148 DF,  p-value: 0.8835

Call:
betareg(formula = MSE_rescaled ~ gender, data = d_sim_gh_adch)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.9453 -0.7459  0.3580  0.9217  1.1737 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.89251    0.07123 -12.531   <2e-16 ***
gender_m    -0.01989    0.06826  -0.291    0.771    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   5.2719     0.5733   9.196   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 58.07 on 3 Df
Pseudo R-squared: 0.0005525
Number of iterations: 13 (BFGS) + 2 (Fisher scoring) 

Religion

Target


Call:
lm(formula = scale(MSE) ~ target, data = d_sim_gh_adch, contrasts = list(target = "contr.sum"))

Residuals:
     Min       1Q   Median       3Q      Max 
-1.87020 -0.30049 -0.00641  0.39469  1.95947 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.01017    0.05327  -0.191 0.848888    
target1     -1.30787    0.15969  -8.190 1.47e-13 ***
target2     -0.55267    0.15969  -3.461 0.000714 ***
target3      0.53573    0.15519   3.452 0.000736 ***
target4      0.55015    0.15519   3.545 0.000534 ***
target5      0.51084    0.16468   3.102 0.002325 ** 
target6      0.50173    0.15969   3.142 0.002049 ** 
target7     -0.95033    0.16468  -5.771 4.86e-08 ***
target8     -0.78709    0.15969  -4.929 2.30e-06 ***
target9      0.40228    0.15969   2.519 0.012888 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.6519 on 140 degrees of freedom
Multiple R-squared:  0.6007,    Adjusted R-squared:  0.5751 
F-statistic: 23.41 on 9 and 140 DF,  p-value: < 2.2e-16

Call:
betareg(formula = MSE_rescaled ~ target, data = d_sim_gh_adch)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-4.3387 -0.3255  0.1168  0.7088  2.5232 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.99365    0.05137 -19.343  < 2e-16 ***
target1     -1.38180    0.19201  -7.196 6.18e-13 ***
target2     -0.59239    0.15996  -3.703 0.000213 ***
target3      0.47928    0.13062   3.669 0.000243 ***
target4      0.55365    0.12986   4.263 2.01e-05 ***
target5      0.53974    0.13765   3.921 8.81e-05 ***
target6      0.54489    0.13357   4.079 4.51e-05 ***
target7     -0.85562    0.17529  -4.881 1.05e-06 ***
target8     -0.59079    0.15990  -3.695 0.000220 ***
target9      0.35280    0.13593   2.595 0.009448 ** 

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   13.508      1.533    8.81   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 126.7 on 11 Df
Pseudo R-squared: 0.606
Number of iterations: 19 (BFGS) + 2 (Fisher scoring) 

All together


Call:
lm(formula = scale(MSE) ~ scale(age) + gender + target, data = d_sim_gh_adch)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.02493 -0.29739 -0.01966  0.37670  1.84631 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.01504    0.05309  -0.283 0.777448    
scale(age)  -0.00289    0.05522  -0.052 0.958341    
gender_m    -0.10258    0.05767  -1.779 0.077482 .  
target1     -1.33761    0.16017  -8.351 6.39e-14 ***
target2     -0.54102    0.15906  -3.401 0.000878 ***
target3      0.50267    0.15564   3.230 0.001549 ** 
target4      0.51660    0.15558   3.320 0.001150 ** 
target5      0.51554    0.16394   3.145 0.002036 ** 
target6      0.51338    0.15906   3.228 0.001560 ** 
target7     -0.93097    0.16425  -5.668 8.11e-08 ***
target8     -0.83032    0.16098  -5.158 8.50e-07 ***
target9      0.45550    0.16237   2.805 0.005754 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.6488 on 138 degrees of freedom
Multiple R-squared:  0.6102,    Adjusted R-squared:  0.5791 
F-statistic: 19.64 on 11 and 138 DF,  p-value: < 2.2e-16
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(MSE) ~ scale(age) + gender + +(1 | target)
   Data: d_sim_gh_adch

REML criterion at convergence: 333.6

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-3.07464 -0.49059  0.01197  0.58928  2.82562 

Random effects:
 Groups   Name        Variance Std.Dev.
 target   (Intercept) 0.6579   0.8111  
 Residual             0.4209   0.6488  
Number of obs: 150, groups:  target, 10

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)  
(Intercept) -1.441e-02  2.619e-01  8.981e+00  -0.055   0.9573  
scale(age)   1.194e-04  5.519e-02  1.382e+02   0.002   0.9983  
gender_m    -9.769e-02  5.753e-02  1.393e+02  -1.698   0.0917 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
           (Intr) scl(g)
scale(age) 0.003        
gender_m   0.011  0.252 

Call:
betareg(formula = MSE_rescaled ~ scale(age) + gender + target, data = d_sim_gh_adch)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-4.6476 -0.3971  0.0891  0.7333  2.5570 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.99718    0.05112 -19.505  < 2e-16 ***
scale(age)   0.02744    0.04935   0.556 0.578109    
gender_m    -0.06856    0.05144  -1.333 0.182585    
target1     -1.39723    0.19174  -7.287 3.16e-13 ***
target2     -0.59003    0.15915  -3.707 0.000209 ***
target3      0.45402    0.13075   3.472 0.000516 ***
target4      0.53200    0.12993   4.095 4.23e-05 ***
target5      0.54282    0.13675   3.969 7.21e-05 ***
target6      0.55044    0.13281   4.145 3.40e-05 ***
target7     -0.83935    0.17426  -4.817 1.46e-06 ***
target8     -0.60878    0.16018  -3.801 0.000144 ***
target9      0.37899    0.13827   2.741 0.006126 ** 

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   13.735      1.559   8.808   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood:   128 on 13 Df
Pseudo R-squared: 0.6152
Number of iterations: 22 (BFGS) + 1 (Fisher scoring) 

Thailand

Age


Call:
lm(formula = scale(MSE) ~ scale(age), data = d_sim_th_adch)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.5399 -0.7272 -0.2931  0.7147  2.9228 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)
(Intercept)  4.658e-17  8.100e-02   0.000    1.000
scale(age)  -9.654e-02  8.127e-02  -1.188    0.237

Residual standard error: 0.9986 on 150 degrees of freedom
Multiple R-squared:  0.00932,   Adjusted R-squared:  0.002716 
F-statistic: 1.411 on 1 and 150 DF,  p-value: 0.2367

Call:
betareg(formula = MSE_rescaled ~ scale(age), data = d_sim_th_adch)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.9606 -0.6910 -0.1969  0.7630  2.6049 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.93266    0.04237  -22.01   <2e-16 ***
scale(age)  -0.05117    0.04194   -1.22    0.222    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   17.059      1.913   8.916   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 129.1 on 3 Df
Pseudo R-squared: 0.01085
Number of iterations: 9 (BFGS) + 2 (Fisher scoring) 

Gender


Call:
lm(formula = scale(MSE) ~ gender, data = d_sim_th_adch)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.6021 -0.7579 -0.1796  0.7213  3.1169 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.008783   0.081551   0.108    0.914
gender_m    0.083441   0.081551   1.023    0.308

Residual standard error: 0.9998 on 150 degrees of freedom
Multiple R-squared:  0.006931,  Adjusted R-squared:  0.0003104 
F-statistic: 1.047 on 1 and 150 DF,  p-value: 0.3079

Call:
betareg(formula = MSE_rescaled ~ gender, data = d_sim_th_adch)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.0166 -0.7391 -0.0938  0.7622  2.7135 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.92876    0.04264 -21.782   <2e-16 ***
gender_m     0.03456    0.04205   0.822    0.411    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   16.964      1.902   8.918   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 128.7 on 3 Df
Pseudo R-squared: 0.004708
Number of iterations: 12 (BFGS) + 2 (Fisher scoring) 

Religion

Target


Call:
lm(formula = scale(MSE) ~ target, data = d_sim_th_adch, contrasts = list(target = "contr.sum"))

Residuals:
    Min      1Q  Median      3Q     Max 
-1.5939 -0.6810 -0.2017  0.4187  2.8156 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)  
(Intercept) -0.001869   0.078903  -0.024   0.9811  
target1     -0.022011   0.238036  -0.092   0.9265  
target2     -0.231547   0.231320  -1.001   0.3185  
target3     -0.019541   0.238036  -0.082   0.9347  
target4     -0.276511   0.238036  -1.162   0.2473  
target5     -0.352161   0.238036  -1.479   0.1412  
target6     -0.109907   0.238036  -0.462   0.6450  
target7     -0.399233   0.238036  -1.677   0.0957 .
target8      0.452051   0.238036   1.899   0.0596 .
target9      0.515583   0.231320   2.229   0.0274 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9725 on 142 degrees of freedom
Multiple R-squared:  0.1107,    Adjusted R-squared:  0.05433 
F-statistic: 1.964 on 9 and 142 DF,  p-value: 0.04774

Call:
betareg(formula = MSE_rescaled ~ target, data = d_sim_th_adch)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.8769 -0.7085 -0.1184  0.5032  2.7180 

Coefficients (mean model with logit link):
             Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.939353   0.040478 -23.206   <2e-16 ***
target1      0.001302   0.120112   0.011   0.9914    
target2     -0.096821   0.118742  -0.815   0.4148    
target3     -0.016916   0.120483  -0.140   0.8883    
target4     -0.163343   0.123739  -1.320   0.1868    
target5     -0.168808   0.123870  -1.363   0.1729    
target6     -0.048193   0.121139  -0.398   0.6908    
target7     -0.232307   0.125437  -1.852   0.0640 .  
target8      0.231012   0.116053   1.991   0.0465 *  
target9      0.237074   0.112724   2.103   0.0355 *  

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   19.003      2.136   8.895   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 137.2 on 11 Df
Pseudo R-squared: 0.116
Number of iterations: 18 (BFGS) + 2 (Fisher scoring) 

All together


Call:
lm(formula = scale(MSE) ~ scale(age) + gender + target, data = d_sim_th_adch)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7560 -0.6538 -0.2126  0.5222  2.9321 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)  
(Intercept)  0.005421   0.079310   0.068   0.9456  
scale(age)  -0.103910   0.079957  -1.300   0.1959  
gender_m     0.068863   0.080833   0.852   0.3957  
target1     -0.047832   0.239150  -0.200   0.8418  
target2     -0.237626   0.231949  -1.024   0.3074  
target3     -0.010505   0.238525  -0.044   0.9649  
target4     -0.283076   0.238106  -1.189   0.2365  
target5     -0.333051   0.238144  -1.399   0.1642  
target6     -0.094453   0.238499  -0.396   0.6927  
target7     -0.389305   0.238023  -1.636   0.1042  
target8      0.458324   0.237920   1.926   0.0561 .
target9      0.515521   0.232123   2.221   0.0280 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9715 on 140 degrees of freedom
Multiple R-squared:  0.1249,    Adjusted R-squared:  0.05617 
F-statistic: 1.817 on 11 and 140 DF,  p-value: 0.05638
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(MSE) ~ scale(age) + gender + +(1 | target)
   Data: d_sim_th_adch

REML criterion at convergence: 435.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.5498 -0.7577 -0.2870  0.6243  3.0218 

Random effects:
 Groups   Name        Variance Std.Dev.
 target   (Intercept) 0.05678  0.2383  
 Residual             0.94357  0.9714  
Number of obs: 152, groups:  target, 10

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept)   0.007629   0.109367   9.128123   0.070    0.946
scale(age)   -0.103718   0.079594 142.376071  -1.303    0.195
gender_m      0.080764   0.080103 144.424864   1.008    0.315

Correlation of Fixed Effects:
           (Intr) scl(g)
scale(age) -0.005       
gender_m    0.078 -0.072

Call:
betareg(formula = MSE_rescaled ~ scale(age) + gender + target, data = d_sim_th_adch)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.1108 -0.7106 -0.1652  0.6362  2.8274 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.93763    0.04040 -23.206   <2e-16 ***
scale(age)  -0.05734    0.04014  -1.428   0.1532    
gender_m     0.02530    0.04055   0.624   0.5327    
target1     -0.01090    0.11989  -0.091   0.9276    
target2     -0.09795    0.11838  -0.827   0.4080    
target3     -0.01588    0.12007  -0.132   0.8948    
target4     -0.16828    0.12307  -1.367   0.1715    
target5     -0.15906    0.12317  -1.291   0.1966    
target6     -0.04316    0.12067  -0.358   0.7206    
target7     -0.23011    0.12479  -1.844   0.0652 .  
target8      0.23550    0.11528   2.043   0.0411 *  
target9      0.24164    0.11245   2.149   0.0316 *  

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   19.303      2.171   8.892   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 138.4 on 13 Df
Pseudo R-squared: 0.1297
Number of iterations: 22 (BFGS) + 1 (Fisher scoring) 

China

Age


Call:
lm(formula = scale(MSE) ~ scale(age), data = d_sim_ch_adch)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7903 -0.7533 -0.1242  0.7703  2.4331 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.000761   0.087667   0.009    0.993
scale(age)  0.093244   0.088050   1.059    0.292

Residual standard error: 0.9995 on 128 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.008685,  Adjusted R-squared:  0.0009408 
F-statistic: 1.121 on 1 and 128 DF,  p-value: 0.2916

Call:
betareg(formula = MSE_rescaled ~ scale(age), data = d_sim_ch_adch)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.8868 -0.6674  0.0562  0.8073  1.9696 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.09024    0.05771 -18.890   <2e-16 ***
scale(age)   0.05319    0.05616   0.947    0.344    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   11.214      1.352   8.294   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 93.52 on 3 Df
Pseudo R-squared: 0.007469
Number of iterations: 14 (BFGS) + 1 (Fisher scoring) 

Gender


Call:
lm(formula = scale(MSE) ~ gender, data = d_sim_ch_adch)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.6607 -0.8233 -0.1631  0.8235  2.3939 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0004534  0.0880196  -0.005    0.996
gender_m    -0.0294729  0.0880196  -0.335    0.738

Residual standard error: 1.003 on 128 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.0008752, Adjusted R-squared:  -0.00693 
F-statistic: 0.1121 on 1 and 128 DF,  p-value: 0.7383

Call:
betareg(formula = MSE_rescaled ~ gender, data = d_sim_ch_adch)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.6994 -0.7133  0.0207  0.8461  1.9501 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.09034    0.05788 -18.837   <2e-16 ***
gender_m    -0.01502    0.05613  -0.268    0.789    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   11.139      1.343   8.296   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood:  93.1 on 3 Df
Pseudo R-squared: 0.000565
Number of iterations: 12 (BFGS) + 2 (Fisher scoring) 

Target


Call:
lm(formula = scale(MSE) ~ target, data = d_sim_ch_adch, contrasts = list(target = "contr.sum"))

Residuals:
     Min       1Q   Median       3Q      Max 
-1.84233 -0.58785 -0.08649  0.58315  2.57595 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)  0.04026    0.11436   0.352  0.72541   
target1      0.01966    0.26276   0.075  0.94050   
target2      0.20515    0.25504   0.804  0.42278   
target3      0.47792    0.86061   0.555  0.57972   
target4     -0.21330    0.28146  -0.758  0.45005   
target5     -0.11062    0.25504  -0.434  0.66528   
target6      0.33042    0.26276   1.257  0.21105   
target7     -0.51317    0.27149  -1.890  0.06117 . 
target8     -0.82181    0.26276  -3.128  0.00222 **
target9      0.20730    0.25504   0.813  0.41795   
target10    -0.32229    0.26276  -1.227  0.22242   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.943 on 119 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.1797,    Adjusted R-squared:  0.1108 
F-statistic: 2.607 on 10 and 119 DF,  p-value: 0.006741

All together


Call:
lm(formula = scale(MSE) ~ scale(age) + gender + target, data = d_sim_ch_adch)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7216 -0.5766 -0.0338  0.5585  2.5411 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)  0.02634    0.11524   0.229  0.81961   
scale(age)   0.07098    0.08755   0.811  0.41914   
gender_m    -0.08365    0.08618  -0.971  0.33370   
target1      0.05474    0.26524   0.206  0.83685   
target2      0.18557    0.25797   0.719  0.47336   
target3      0.33288    0.87154   0.382  0.70320   
target4     -0.15307    0.28679  -0.534  0.59454   
target5     -0.06450    0.25869  -0.249  0.80355   
target6      0.33335    0.26414   1.262  0.20946   
target7     -0.53974    0.27314  -1.976  0.05050 . 
target8     -0.82533    0.26377  -3.129  0.00221 **
target9      0.25342    0.25886   0.979  0.32961   
target10    -0.31938    0.26620  -1.200  0.23264   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9453 on 117 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.1895,    Adjusted R-squared:  0.1063 
F-statistic: 2.279 on 12 and 117 DF,  p-value: 0.01223
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(MSE) ~ scale(age) + gender + +(1 | target)
   Data: d_sim_ch_adch

REML criterion at convergence: 369.6

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.6602 -0.7118 -0.1095  0.6741  2.5098 

Random effects:
 Groups   Name        Variance Std.Dev.
 target   (Intercept) 0.1285   0.3585  
 Residual             0.8890   0.9429  
Number of obs: 130, groups:  target, 11

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept)   0.001534   0.139281   9.354759   0.011    0.991
scale(age)    0.082578   0.085695 123.487072   0.964    0.337
gender_m     -0.069777   0.084725 122.182416  -0.824    0.412

Correlation of Fixed Effects:
           (Intr) scl(g)
scale(age) -0.004       
gender_m    0.017 -0.122

Vanuatu

Age


Call:
lm(formula = scale(MSE) ~ scale(age), data = d_sim_vt_adch)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.8608 -0.9267  0.1151  0.8844  1.4462 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)  0.01157    0.08368   0.138    0.890
scale(age)   0.02228    0.08398   0.265    0.791

Residual standard error: 0.9972 on 140 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.0005025, Adjusted R-squared:  -0.006637 
F-statistic: 0.07038 on 1 and 140 DF,  p-value: 0.7912

Call:
betareg(formula = MSE_rescaled ~ scale(age), data = d_sim_vt_adch)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.1805 -0.8564  0.1844  0.8695  1.3754 

Coefficients (mean model with logit link):
             Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.430248   0.048815  -8.814   <2e-16 ***
scale(age)   0.006664   0.048773   0.137    0.891    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)    11.32       1.29   8.771   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 80.22 on 3 Df
Pseudo R-squared: 0.0001346
Number of iterations: 9 (BFGS) + 2 (Fisher scoring) 

Gender


Call:
lm(formula = scale(MSE) ~ gender, data = d_sim_vt_adch)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.86647 -0.90478  0.09592  0.88104  1.46081 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.008161   0.084711  -0.096    0.923
gender_m    -0.055574   0.084711  -0.656    0.513

Residual standard error: 1.002 on 141 degrees of freedom
Multiple R-squared:  0.003043,  Adjusted R-squared:  -0.004028 
F-statistic: 0.4304 on 1 and 141 DF,  p-value: 0.5129

Call:
betareg(formula = MSE_rescaled ~ gender, data = d_sim_vt_adch)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.1830 -0.8390  0.1678  0.8723  1.3920 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.44208    0.04950  -8.931   <2e-16 ***
gender_m    -0.03318    0.04928  -0.673    0.501    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   11.198      1.272   8.805   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 80.26 on 3 Df
Pseudo R-squared: 0.003177
Number of iterations: 11 (BFGS) + 2 (Fisher scoring) 

Target


Call:
lm(formula = scale(MSE) ~ target, data = d_sim_vt_adch, contrasts = list(target = "contr.sum"))

Residuals:
     Min       1Q   Median       3Q      Max 
-2.22143 -0.59576  0.05297  0.63392  2.01145 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.02832    0.07872   0.360  0.71964    
target1     -0.67395    0.23952  -2.814  0.00565 ** 
target2     -0.51665    0.23176  -2.229  0.02749 *  
target3      0.29425    0.29892   0.984  0.32672    
target4      0.38686    0.34215   1.131  0.26025    
target5      0.23323    0.22483   1.037  0.30145    
target6      0.36705    0.23176   1.584  0.11565    
target7      0.55082    0.21857   2.520  0.01293 *  
target8     -0.95911    0.22483  -4.266 3.77e-05 ***
target9     -0.13908    0.22483  -0.619  0.53723    
target10     0.09963    0.23952   0.416  0.67810    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9017 on 132 degrees of freedom
Multiple R-squared:  0.2442,    Adjusted R-squared:  0.1869 
F-statistic: 4.265 on 10 and 132 DF,  p-value: 3.732e-05

Call:
betareg(formula = MSE_rescaled ~ target, data = d_sim_vt_adch)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.0861 -0.6609  0.1370  0.7399  2.2219 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.42754    0.04514  -9.472  < 2e-16 ***
target1     -0.42361    0.14315  -2.959  0.00309 ** 
target2     -0.31615    0.13636  -2.318  0.02043 *  
target3      0.19121    0.16786   1.139  0.25467    
target4      0.24399    0.19166   1.273  0.20300    
target5      0.13825    0.12671   1.091  0.27527    
target6      0.21419    0.13015   1.646  0.09981 .  
target7      0.31819    0.12241   2.599  0.00934 ** 
target8     -0.56957    0.13749  -4.143 3.43e-05 ***
target9     -0.06163    0.12854  -0.479  0.63159    
target10     0.05224    0.13569   0.385  0.70023    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   14.787      1.696   8.716   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood:   100 on 12 Df
Pseudo R-squared: 0.2479
Number of iterations: 21 (BFGS) + 2 (Fisher scoring) 

All together


Call:
lm(formula = scale(MSE) ~ scale(age) + gender + target, data = d_sim_vt_adch)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.23284 -0.57970  0.03138  0.58494  1.98058 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.03605    0.07853   0.459  0.64698    
scale(age)  -0.00728    0.07643  -0.095  0.92427    
gender_m    -0.04021    0.07775  -0.517  0.60588    
target1     -0.68118    0.23828  -2.859  0.00496 ** 
target2     -0.51908    0.22956  -2.261  0.02542 *  
target3      0.27485    0.29517   0.931  0.35352    
target4      0.38024    0.33757   1.126  0.26209    
target5      0.22451    0.22239   1.010  0.31461    
target6      0.34737    0.22874   1.519  0.13130    
target7      0.67889    0.22244   3.052  0.00276 ** 
target8     -0.98538    0.22306  -4.418 2.09e-05 ***
target9     -0.16564    0.22303  -0.743  0.45903    
target10     0.10364    0.23916   0.433  0.66548    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8885 on 129 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.2688,    Adjusted R-squared:  0.2008 
F-statistic: 3.953 on 12 and 129 DF,  p-value: 3.458e-05
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(MSE) ~ scale(age) + gender + +(1 | target)
   Data: d_sim_vt_adch

REML criterion at convergence: 391.1

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.44409 -0.81803  0.08568  0.71906  2.05346 

Random effects:
 Groups   Name        Variance Std.Dev.
 target   (Intercept) 0.2182   0.4672  
 Residual             0.7880   0.8877  
Number of obs: 142, groups:  target, 11

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)
(Intercept)   0.025394   0.160746  10.417215   0.158    0.877
scale(age)   -0.000469   0.076012 131.630258  -0.006    0.995
gender_m     -0.041024   0.077200 132.360377  -0.531    0.596

Correlation of Fixed Effects:
           (Intr) scl(g)
scale(age) -0.001       
gender_m    0.070 -0.035

Call:
betareg(formula = MSE_rescaled ~ scale(age) + gender + target, data = d_sim_vt_adch)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.2043 -0.6984  0.0942  0.7788  2.3123 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.42257    0.04466  -9.463  < 2e-16 ***
scale(age)  -0.00811    0.04345  -0.187  0.85194    
gender_m    -0.01615    0.04421  -0.365  0.71491    
target1     -0.43278    0.14123  -3.064  0.00218 ** 
target2     -0.32141    0.13397  -2.399  0.01644 *  
target3      0.17955    0.16438   1.092  0.27470    
target4      0.23957    0.18750   1.278  0.20134    
target5      0.13341    0.12430   1.073  0.28316    
target6      0.20318    0.12737   1.595  0.11068    
target7      0.40402    0.12340   3.274  0.00106 ** 
target8     -0.58371    0.13530  -4.314  1.6e-05 ***
target9     -0.07637    0.12648  -0.604  0.54598    
target10     0.05178    0.13439   0.385  0.70005    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   15.545      1.792   8.674   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 102.6 on 14 Df
Pseudo R-squared: 0.2747
Number of iterations: 22 (BFGS) + 2 (Fisher scoring) 

Using US model for other adults (kinda = yes = 1)

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Ghana

Age


Call:
lm(formula = scale(MSE) ~ scale(age), data = d_sim_us_gh)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.2684 -0.9444 -0.1291  1.0319  1.8814 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept) -0.02676    0.08152  -0.328   0.7432  
scale(age)  -0.18392    0.08180  -2.248   0.0261 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.985 on 144 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.03392,   Adjusted R-squared:  0.02721 
F-statistic: 5.055 on 1 and 144 DF,  p-value: 0.02607

Call:
betareg(formula = MSE_rescaled ~ scale(age), data = d_sim_us_gh)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.3607 -0.9458  0.1580  0.9800  1.4586 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.05011    0.07873 -13.339   <2e-16 ***
scale(age)  -0.14764    0.07474  -1.975   0.0482 *  

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   4.6320     0.5147       9   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood:  63.1 on 3 Df
Pseudo R-squared: 0.02768
Number of iterations: 12 (BFGS) + 1 (Fisher scoring) 

Gender


Call:
lm(formula = scale(MSE) ~ gender, data = d_sim_us_gh)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.14287 -0.86431 -0.03598  1.04135  1.66985 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept) -0.01671    0.08172  -0.205   0.8382  
gender_m    -0.13928    0.08172  -1.704   0.0904 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9937 on 148 degrees of freedom
Multiple R-squared:  0.01925,   Adjusted R-squared:  0.01262 
F-statistic: 2.904 on 1 and 148 DF,  p-value: 0.09043

Call:
betareg(formula = MSE_rescaled ~ gender, data = d_sim_us_gh)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.1781 -0.8583  0.2286  0.9768  1.3478 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.03667    0.07848 -13.209   <2e-16 ***
gender_m    -0.10650    0.07351  -1.449    0.147    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   4.5516     0.4974   9.152   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 62.04 on 3 Df
Pseudo R-squared: 0.01585
Number of iterations: 13 (BFGS) + 1 (Fisher scoring) 

Race/ethnicity


Call:
lm(formula = scale(MSE) ~ ethnicity_cat2, data = d_sim_us_gh)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.0738 -0.9378 -0.1646  1.0779  1.4607 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)
(Intercept)             -0.01451    0.08367  -0.173    0.863
ethnicity_cat2_nonFante -0.06803    0.08367  -0.813    0.417

Residual standard error: 1.001 on 148 degrees of freedom
Multiple R-squared:  0.004447,  Adjusted R-squared:  -0.00228 
F-statistic: 0.6611 on 1 and 148 DF,  p-value: 0.4175

Call:
betareg(formula = MSE_rescaled ~ ethnicity_cat2, data = d_sim_us_gh)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.0986 -0.9205  0.1272  0.9904  1.2336 

Coefficients (mean model with logit link):
                        Estimate Std. Error z value Pr(>|z|)    
(Intercept)             -1.03527    0.07985 -12.966   <2e-16 ***
ethnicity_cat2_nonFante -0.06020    0.07486  -0.804    0.421    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)    4.506      0.492   9.159   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 61.31 on 3 Df
Pseudo R-squared: 0.00493
Number of iterations: 12 (BFGS) + 1 (Fisher scoring) 

Education


Call:
lm(formula = scale(MSE) ~ scale(education_catX), data = d_sim_us_gh %>% 
    mutate(education_catX = as.numeric(education_catX)))

Residuals:
    Min      1Q  Median      3Q     Max 
-1.0867 -1.0339 -0.2125  1.1076  1.5410 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)
(Intercept)            0.006848   0.081976   0.084    0.934
scale(education_catX) -0.071715   0.082253  -0.872    0.385

Residual standard error: 1.001 on 147 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.005145,  Adjusted R-squared:  -0.001623 
F-statistic: 0.7602 on 1 and 147 DF,  p-value: 0.3847

Call:
betareg(formula = MSE_rescaled ~ scale(education_catX), data = d_sim_us_gh %>% mutate(education_catX = as.numeric(education_catX)))

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.1264 -1.0452  0.0822  1.0219  1.2991 

Coefficients (mean model with logit link):
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)           -1.01661    0.07802 -13.029   <2e-16 ***
scale(education_catX) -0.07037    0.07395  -0.952    0.341    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   4.5143     0.4943   9.132   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 60.58 on 3 Df
Pseudo R-squared: 0.006762
Number of iterations: 12 (BFGS) + 2 (Fisher scoring) 


Call:
lm(formula = scale(MSE) ~ education_cat2, data = d_sim_us_gh)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.1620 -0.9139 -0.1157  1.0655  1.5321 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)
(Intercept)        0.01767    0.08187   0.216    0.829
education_cat2_hs -0.12403    0.08187  -1.515    0.132

Residual standard error: 0.9955 on 147 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.01537,   Adjusted R-squared:  0.008677 
F-statistic: 2.295 on 1 and 147 DF,  p-value: 0.1319

Call:
betareg(formula = MSE_rescaled ~ education_cat2, data = d_sim_us_gh)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.2308 -0.8942  0.1770  0.9836  1.2787 

Coefficients (mean model with logit link):
                  Estimate Std. Error z value Pr(>|z|)    
(Intercept)       -1.00834    0.07782 -12.957   <2e-16 ***
education_cat2_hs -0.11043    0.07311  -1.511    0.131    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   4.5572     0.4994   9.125   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 61.26 on 3 Df
Pseudo R-squared: 0.01691
Number of iterations: 13 (BFGS) + 1 (Fisher scoring) 

Rural/urban


Call:
lm(formula = scale(MSE) ~ urban_rural_cat2, data = d_sim_us_gh)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.2025 -0.9117 -0.2291  1.0373  1.6224 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)  
(Intercept)             0.03683    0.08384   0.439    0.661  
urban_rural_cat2_rural  0.14538    0.08384   1.734    0.085 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9933 on 148 degrees of freedom
Multiple R-squared:  0.01991,   Adjusted R-squared:  0.01329 
F-statistic: 3.007 on 1 and 148 DF,  p-value: 0.085

Call:
betareg(formula = MSE_rescaled ~ urban_rural_cat2, data = d_sim_us_gh)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.3064 -0.8883  0.0595  0.9674  1.3400 

Coefficients (mean model with logit link):
                       Estimate Std. Error z value Pr(>|z|)    
(Intercept)            -0.99079    0.07895 -12.549   <2e-16 ***
urban_rural_cat2_rural  0.13563    0.07460   1.818    0.069 .  

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   4.5878     0.5016   9.146   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood:  62.6 on 3 Df
Pseudo R-squared: 0.02371
Number of iterations: 13 (BFGS) + 1 (Fisher scoring) 

Religion

Target


Call:
lm(formula = scale(MSE) ~ target, data = d_sim_us_gh, contrasts = list(target = "contr.sum"))

Residuals:
     Min       1Q   Median       3Q      Max 
-1.38302 -0.18471  0.00000  0.09013  1.88009 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept) -2.448e-16  4.120e-02   0.000  1.00000    
target1     -1.020e+00  1.236e-01  -8.254 1.02e-13 ***
target2     -1.020e+00  1.236e-01  -8.254 1.02e-13 ***
target3     -1.565e-01  1.236e-01  -1.266  0.20764    
target4      3.627e-01  1.236e-01   2.934  0.00391 ** 
target5      9.190e-01  1.236e-01   7.435 9.50e-12 ***
target6      1.256e+00  1.236e-01  10.165  < 2e-16 ***
target7     -1.050e-01  1.236e-01  -0.849  0.39726    
target8     -1.008e+00  1.236e-01  -8.157 1.77e-13 ***
target9     -4.999e-01  1.236e-01  -4.044 8.64e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.5046 on 140 degrees of freedom
Multiple R-squared:  0.7607,    Adjusted R-squared:  0.7454 
F-statistic: 49.46 on 9 and 140 DF,  p-value: < 2.2e-16

Call:
betareg(formula = MSE_rescaled ~ target, data = d_sim_us_gh)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.8507 -0.2602  0.0000  0.2276  3.0270 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.20755    0.04864 -24.829  < 2e-16 ***
target1     -1.12750    0.17136  -6.580 4.72e-11 ***
target2     -1.12750    0.17136  -6.580 4.72e-11 ***
target3     -0.04954    0.13161  -0.376    0.707    
target4      0.51130    0.12017   4.255 2.09e-05 ***
target5      1.01276    0.11573   8.751  < 2e-16 ***
target6      1.26792    0.11554  10.974  < 2e-16 ***
target7     -0.08394    0.13254  -0.633    0.526    
target8     -1.10397    0.17031  -6.482 9.04e-11 ***
target9     -0.57896    0.14857  -3.897 9.74e-05 ***

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   18.419      2.115   8.707   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 162.1 on 11 Df
Pseudo R-squared: 0.7615
Number of iterations: 20 (BFGS) + 1 (Fisher scoring) 

All together


Call:
lm(formula = scale(MSE) ~ scale(age) + gender + scale(education_catX) + 
    ethnicity_cat2 + urban_rural_cat2 + target, data = d_sim_us_gh %>% 
    mutate(education_catX = as.numeric(education_catX)))

Residuals:
     Min       1Q   Median       3Q      Max 
-1.24912 -0.15051 -0.01104  0.12924  1.83348 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)              0.002067   0.045563   0.045 0.963892    
scale(age)              -0.051117   0.047703  -1.072 0.285908    
gender_m                 0.018945   0.045660   0.415 0.678881    
scale(education_catX)    0.027478   0.050714   0.542 0.588864    
ethnicity_cat2_nonFante -0.058514   0.047792  -1.224 0.223035    
urban_rural_cat2_rural   0.058046   0.055038   1.055 0.293544    
target1                 -0.953915   0.132597  -7.194 4.45e-11 ***
target2                 -1.040403   0.128308  -8.109 3.32e-13 ***
target3                 -0.146720   0.129901  -1.129 0.260777    
target4                  0.332184   0.130571   2.544 0.012127 *  
target5                  0.925414   0.133589   6.927 1.78e-10 ***
target6                  1.225074   0.136186   8.996 2.43e-15 ***
target7                 -0.092543   0.128999  -0.717 0.474418    
target8                 -1.007726   0.132126  -7.627 4.49e-12 ***
target9                 -0.502963   0.130329  -3.859 0.000178 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.5138 on 130 degrees of freedom
  (5 observations deleted due to missingness)
Multiple R-squared:  0.7611,    Adjusted R-squared:  0.7354 
F-statistic: 29.58 on 14 and 130 DF,  p-value: < 2.2e-16

Call:
lm(formula = scale(MSE) ~ scale(age) + gender + education_cat2 + 
    ethnicity_cat2 + urban_rural_cat2 + target, data = d_sim_us_gh)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.25597 -0.13694 -0.00908  0.12961  1.84592 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)              0.000315   0.045445   0.007 0.994480    
scale(age)              -0.046497   0.047768  -0.973 0.332165    
gender_m                 0.019951   0.045844   0.435 0.664138    
education_cat2_hs        0.019182   0.053122   0.361 0.718617    
ethnicity_cat2_nonFante -0.054516   0.046884  -1.163 0.247051    
urban_rural_cat2_rural   0.053244   0.055421   0.961 0.338481    
target1                 -0.957406   0.132451  -7.228 3.72e-11 ***
target2                 -1.037976   0.128361  -8.086 3.74e-13 ***
target3                 -0.147210   0.130159  -1.131 0.260137    
target4                  0.333238   0.130849   2.547 0.012039 *  
target5                  0.928684   0.133442   6.959 1.51e-10 ***
target6                  1.231436   0.137476   8.957 3.01e-15 ***
target7                 -0.096081   0.131384  -0.731 0.465914    
target8                 -1.009013   0.132177  -7.634 4.33e-12 ***
target9                 -0.509947   0.129675  -3.933 0.000136 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.5141 on 130 degrees of freedom
  (5 observations deleted due to missingness)
Multiple R-squared:  0.7608,    Adjusted R-squared:  0.735 
F-statistic: 29.53 on 14 and 130 DF,  p-value: < 2.2e-16
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(MSE) ~ scale(age) + gender + scale(education_catX) + ethnicity_cat2 +  
    urban_rural_cat2 + +(1 | target)
   Data: d_sim_us_gh %>% mutate(education_catX = as.numeric(education_catX))

REML criterion at convergence: 272.4

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.4160 -0.2900 -0.0277  0.2694  3.5324 

Random effects:
 Groups   Name        Variance Std.Dev.
 target   (Intercept) 0.7958   0.8921  
 Residual             0.2640   0.5138  
Number of obs: 145, groups:  target, 10

Fixed effects:
                          Estimate Std. Error         df t value Pr(>|t|)
(Intercept)               0.001911   0.285760   9.017062   0.007    0.995
scale(age)               -0.055075   0.047641 130.669246  -1.156    0.250
gender_m                  0.016428   0.045624 130.408826   0.360    0.719
scale(education_catX)     0.028365   0.050690 130.235946   0.560    0.577
ethnicity_cat2_nonFante  -0.060290   0.047729 130.680068  -1.263    0.209
urban_rural_cat2_rural    0.062004   0.054983 130.512288   1.128    0.262

Correlation of Fixed Effects:
            (Intr) scl(g) gndr_m sc(_X) et_2_F
scale(age)  -0.009                            
gender_m     0.018 -0.220                     
scl(dctn_X)  0.013 -0.104  0.015              
ethncty_2_F  0.025  0.106 -0.047 -0.221       
urbn_rrl_2_  0.040 -0.250  0.074  0.508 -0.220
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(MSE) ~ scale(age) + gender + education_cat2 + ethnicity_cat2 +  
    urban_rural_cat2 + +(1 | target)
   Data: d_sim_us_gh

REML criterion at convergence: 272.6

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.4302 -0.2740 -0.0384  0.2519  3.5530 

Random effects:
 Groups   Name        Variance Std.Dev.
 target   (Intercept) 0.8002   0.8945  
 Residual             0.2643   0.5141  
Number of obs: 145, groups:  target, 10

Fixed effects:
                          Estimate Std. Error         df t value Pr(>|t|)
(Intercept)              6.155e-05  2.865e-01  9.011e+00   0.000    1.000
scale(age)              -5.060e-02  4.770e-02  1.307e+02  -1.061    0.291
gender_m                 1.726e-02  4.581e-02  1.304e+02   0.377    0.707
education_cat2_hs        1.688e-02  5.307e-02  1.305e+02   0.318    0.751
ethnicity_cat2_nonFante -5.590e-02  4.682e-02  1.307e+02  -1.194    0.235
urban_rural_cat2_rural   5.547e-02  5.537e-02  1.305e+02   1.002    0.318

Correlation of Fixed Effects:
            (Intr) scl(g) gndr_m edc_2_ et_2_F
scale(age)  -0.008                            
gender_m     0.018 -0.208                     
edctn_ct2_h  0.003  0.110  0.083              
ethncty_2_F  0.029  0.074 -0.053 -0.102       
urbn_rrl_2_  0.034 -0.139  0.108  0.517 -0.161

Call:
betareg(formula = MSE_rescaled ~ scale(age) + gender + scale(education_catX) + ethnicity_cat2 + 
    urban_rural_cat2 + target, data = d_sim_us_gh %>% mutate(education_catX = as.numeric(education_catX)))

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.4591 -0.2235 -0.0073  0.2487  3.0691 

Coefficients (mean model with logit link):
                        Estimate Std. Error z value Pr(>|z|)    
(Intercept)             -1.20724    0.05190 -23.260  < 2e-16 ***
scale(age)              -0.04927    0.04894  -1.007 0.314064    
gender_m                 0.03805    0.04819   0.790 0.429753    
scale(education_catX)    0.03536    0.05442   0.650 0.515900    
ethnicity_cat2_nonFante -0.07092    0.05079  -1.396 0.162588    
urban_rural_cat2_rural   0.05223    0.05761   0.907 0.364650    
target1                 -1.06368    0.17637  -6.031 1.63e-09 ***
target2                 -1.16250    0.17355  -6.698 2.11e-11 ***
target3                 -0.01660    0.13537  -0.123 0.902419    
target4                  0.48504    0.12517   3.875 0.000107 ***
target5                  1.01682    0.12311   8.260  < 2e-16 ***
target6                  1.23652    0.12461   9.923  < 2e-16 ***
target7                 -0.07233    0.13549  -0.534 0.593427    
target8                 -1.11796    0.17766  -6.293 3.12e-10 ***
target9                 -0.58252    0.15200  -3.832 0.000127 ***

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   18.475      2.159   8.557   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 157.8 on 16 Df
Pseudo R-squared: 0.7615
Number of iterations: 24 (BFGS) + 2 (Fisher scoring) 

Call:
betareg(formula = MSE_rescaled ~ scale(age) + gender + education_cat2 + ethnicity_cat2 + 
    urban_rural_cat2 + target, data = d_sim_us_gh %>% mutate(education_catX = as.numeric(education_catX)))

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.4775 -0.2591  0.0059  0.2671  3.0730 

Coefficients (mean model with logit link):
                        Estimate Std. Error z value Pr(>|z|)    
(Intercept)             -1.20900    0.05186 -23.315  < 2e-16 ***
scale(age)              -0.04357    0.04889  -0.891 0.372797    
gender_m                 0.03892    0.04841   0.804 0.421395    
education_cat2_hs        0.02452    0.05498   0.446 0.655619    
ethnicity_cat2_nonFante -0.06402    0.04916  -1.302 0.192781    
urban_rural_cat2_rural   0.04500    0.05695   0.790 0.429383    
target1                 -1.06732    0.17629  -6.054 1.41e-09 ***
target2                 -1.15787    0.17348  -6.675 2.48e-11 ***
target3                 -0.01637    0.13569  -0.121 0.903967    
target4                  0.48449    0.12549   3.861 0.000113 ***
target5                  1.02059    0.12295   8.301  < 2e-16 ***
target6                  1.24470    0.12612   9.869  < 2e-16 ***
target7                 -0.07905    0.13794  -0.573 0.566600    
target8                 -1.11841    0.17770  -6.294 3.10e-10 ***
target9                 -0.59084    0.15152  -3.900 9.64e-05 ***

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   18.438      2.155   8.557   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 157.7 on 16 Df
Pseudo R-squared: 0.7614
Number of iterations: 24 (BFGS) + 2 (Fisher scoring) 

Thailand

Age


Call:
lm(formula = scale(MSE) ~ scale(age), data = d_sim_us_th)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.8652 -0.8126  0.1061  0.9638  1.4156 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.002823   0.082393   0.034    0.973
scale(age)  0.029688   0.082671   0.359    0.720

Residual standard error: 1.006 on 147 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.0008765, Adjusted R-squared:  -0.00592 
F-statistic: 0.129 on 1 and 147 DF,  p-value: 0.72

Call:
betareg(formula = MSE_rescaled ~ scale(age), data = d_sim_us_th)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.3632 -0.5967  0.2285  0.9066  1.2356 

Coefficients (mean model with logit link):
             Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.667272   0.057801 -11.544   <2e-16 ***
scale(age)   0.009412   0.057056   0.165    0.869    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   7.8806     0.8674   9.085   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 68.03 on 3 Df
Pseudo R-squared: 0.0001609
Number of iterations: 12 (BFGS) + 2 (Fisher scoring) 

Gender


Call:
lm(formula = scale(MSE) ~ gender, data = d_sim_us_th)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.87720 -0.68196  0.04427  0.92086  1.39444 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept)  -0.4503     0.2404  -1.873   0.0630 .
gender_m      0.4007     0.2515   1.593   0.1132  
gender_o     -0.9159     0.4710  -1.944   0.0538 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9923 on 147 degrees of freedom
Multiple R-squared:  0.02853,   Adjusted R-squared:  0.01531 
F-statistic: 2.159 on 2 and 147 DF,  p-value: 0.1191

Call:
betareg(formula = MSE_rescaled ~ gender, data = d_sim_us_th)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.4240 -0.5098  0.1910  0.8823  1.2392 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -0.9693     0.1918  -5.054 4.32e-07 ***
gender_m      0.2776     0.1979   1.402    0.161    
gender_o     -0.6122     0.3766  -1.625    0.104    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   8.1244     0.8928     9.1   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 70.72 on 4 Df
Pseudo R-squared: 0.02693
Number of iterations: 15 (BFGS) + 1 (Fisher scoring) 

Race/ethnicity

Education


Call:
lm(formula = scale(MSE) ~ scale(education_catX), data = d_sim_us_th %>% 
    mutate(education_catX = as.numeric(education_catX)))

Residuals:
    Min      1Q  Median      3Q     Max 
-1.9723 -0.8418  0.1319  0.9013  1.5219 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)
(Intercept)           -0.01392    0.08298  -0.168    0.867
scale(education_catX) -0.13110    0.08327  -1.574    0.118

Residual standard error: 0.9992 on 143 degrees of freedom
  (5 observations deleted due to missingness)
Multiple R-squared:  0.01704,   Adjusted R-squared:  0.01016 
F-statistic: 2.479 on 1 and 143 DF,  p-value: 0.1176

Call:
betareg(formula = MSE_rescaled ~ scale(education_catX), data = d_sim_us_th %>% mutate(education_catX = as.numeric(education_catX)))

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.5294 -0.6501  0.2511  0.8554  1.3312 

Coefficients (mean model with logit link):
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)           -0.67935    0.05850 -11.612   <2e-16 ***
scale(education_catX) -0.08343    0.05760  -1.448    0.148    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   7.9553     0.8884   8.955   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 67.34 on 3 Df
Pseudo R-squared: 0.01378
Number of iterations: 12 (BFGS) + 2 (Fisher scoring) 


Call:
lm(formula = scale(MSE) ~ education_cat2, data = d_sim_us_th)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.9310 -0.8785  0.1087  0.9373  1.4996 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)          0.005588   0.084440   0.066    0.947
education_cat2_coll -0.113134   0.084440  -1.340    0.182

Residual standard error: 1.002 on 143 degrees of freedom
  (5 observations deleted due to missingness)
Multiple R-squared:  0.0124,    Adjusted R-squared:  0.005491 
F-statistic: 1.795 on 1 and 143 DF,  p-value: 0.1824

Call:
betareg(formula = MSE_rescaled ~ education_cat2, data = d_sim_us_th)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.4762 -0.6956  0.2506  0.8834  1.3147 

Coefficients (mean model with logit link):
                    Estimate Std. Error z value Pr(>|z|)    
(Intercept)         -0.66633    0.05924 -11.248   <2e-16 ***
education_cat2_coll -0.07441    0.05837  -1.275    0.202    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   7.9288     0.8852   8.957   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 67.09 on 3 Df
Pseudo R-squared: 0.01053
Number of iterations: 12 (BFGS) + 2 (Fisher scoring) 

Rural/urban


Call:
lm(formula = scale(MSE) ~ urban_rural_cat2, data = d_sim_us_th)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.8234 -0.7815  0.1009  0.9694  1.3836 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)
(Intercept)            -0.01021    0.08940  -0.114    0.909
urban_rural_cat2_rural  0.02133    0.08940   0.239    0.812

Residual standard error: 0.9954 on 139 degrees of freedom
  (9 observations deleted due to missingness)
Multiple R-squared:  0.0004094, Adjusted R-squared:  -0.006782 
F-statistic: 0.05693 on 1 and 139 DF,  p-value: 0.8118

Call:
betareg(formula = MSE_rescaled ~ urban_rural_cat2, data = d_sim_us_th)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.3571 -0.5830  0.2253  0.9259  1.2419 

Coefficients (mean model with logit link):
                         Estimate Std. Error z value Pr(>|z|)    
(Intercept)            -0.6702905  0.0625967 -10.708   <2e-16 ***
urban_rural_cat2_rural  0.0008962  0.0617602   0.015    0.988    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   8.0807     0.9154   8.828   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 65.85 on 3 Df
Pseudo R-squared: 1.428e-06
Number of iterations: 12 (BFGS) + 2 (Fisher scoring) 

Religion

Target


Call:
lm(formula = scale(MSE) ~ target, data = d_sim_us_th, contrasts = list(target = "contr.sum"))

Residuals:
    Min      1Q  Median      3Q     Max 
-2.1383 -0.6608  0.0856  0.7984  1.7641 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  3.626e-17  7.663e-02   0.000   1.0000    
target1      3.058e-01  2.299e-01   1.330   0.1856    
target2      2.844e-01  2.299e-01   1.237   0.2181    
target3      4.476e-01  2.299e-01   1.947   0.0535 .  
target4     -1.659e-01  2.299e-01  -0.722   0.4718    
target5      7.442e-02  2.299e-01   0.324   0.7467    
target6     -2.284e-01  2.299e-01  -0.993   0.3223    
target7     -1.065e+00  2.299e-01  -4.634 8.12e-06 ***
target8      3.259e-01  2.299e-01   1.418   0.1585    
target9      8.166e-02  2.299e-01   0.355   0.7230    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9386 on 140 degrees of freedom
Multiple R-squared:  0.1723,    Adjusted R-squared:  0.1191 
F-statistic: 3.238 on 9 and 140 DF,  p-value: 0.001335

Call:
betareg(formula = MSE_rescaled ~ target, data = d_sim_us_th)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.1430 -0.5794  0.1988  0.8286  1.6591 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.67999    0.05348 -12.715  < 2e-16 ***
target1      0.19111    0.15426   1.239   0.2154    
target2      0.16601    0.15458   1.074   0.2829    
target3      0.33224    0.15279   2.174   0.0297 *  
target4     -0.08796    0.15868  -0.554   0.5794    
target5      0.09443    0.15557   0.607   0.5439    
target6     -0.10005    0.15892  -0.630   0.5290    
target7     -0.78145    0.17758  -4.401 1.08e-05 ***
target8      0.22629    0.15385   1.471   0.1413    
target9      0.01213    0.15687   0.077   0.9383    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)    9.561      1.059   9.026   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 82.79 on 11 Df
Pseudo R-squared: 0.1741
Number of iterations: 21 (BFGS) + 2 (Fisher scoring) 

All together


Call:
lm(formula = scale(MSE) ~ scale(age) + gender + scale(education_catX) + 
    urban_rural_cat2 + target, data = d_sim_us_th %>% mutate(education_catX = as.numeric(education_catX)))

Residuals:
     Min       1Q   Median       3Q      Max 
-2.38074 -0.61920  0.04159  0.68964  1.78708 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)            -0.412495   0.234763  -1.757   0.0814 .  
scale(age)             -0.050702   0.098004  -0.517   0.6059    
gender_m                0.372744   0.243310   1.532   0.1281    
gender_o               -0.817959   0.458864  -1.783   0.0772 .  
scale(education_catX)  -0.166997   0.099411  -1.680   0.0956 .  
urban_rural_cat2_rural -0.019313   0.089466  -0.216   0.8294    
target1                 0.386281   0.243392   1.587   0.1151    
target2                 0.155380   0.258712   0.601   0.5492    
target3                 0.468897   0.234203   2.002   0.0475 *  
target4                -0.005458   0.241182  -0.023   0.9820    
target5                 0.046755   0.248550   0.188   0.8511    
target6                -0.128337   0.236007  -0.544   0.5876    
target7                -0.983734   0.238600  -4.123 6.89e-05 ***
target8                 0.368886   0.240734   1.532   0.1280    
target9                -0.130218   0.274256  -0.475   0.6358    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9327 on 121 degrees of freedom
  (14 observations deleted due to missingness)
Multiple R-squared:  0.2146,    Adjusted R-squared:  0.1237 
F-statistic: 2.361 on 14 and 121 DF,  p-value: 0.006243

Call:
lm(formula = scale(MSE) ~ scale(age) + gender + education_cat2 + 
    urban_rural_cat2 + target, data = d_sim_us_th)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.35168 -0.64247  0.05175  0.69392  1.75683 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)    
(Intercept)            -0.39817    0.23608  -1.687   0.0943 .  
scale(age)             -0.04986    0.10228  -0.487   0.6268    
gender_m                0.38275    0.24407   1.568   0.1194    
gender_o               -0.85183    0.46028  -1.851   0.0667 .  
education_cat2_coll    -0.15481    0.10712  -1.445   0.1510    
urban_rural_cat2_rural -0.02897    0.09148  -0.317   0.7520    
target1                 0.39454    0.24553   1.607   0.1107    
target2                 0.13913    0.26312   0.529   0.5979    
target3                 0.45856    0.23478   1.953   0.0531 .  
target4                -0.02524    0.24092  -0.105   0.9167    
target5                 0.05631    0.24920   0.226   0.8216    
target6                -0.12212    0.23673  -0.516   0.6069    
target7                -0.97545    0.23967  -4.070 8.42e-05 ***
target8                 0.36409    0.24140   1.508   0.1341    
target9                -0.11703    0.27463  -0.426   0.6708    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9355 on 121 degrees of freedom
  (14 observations deleted due to missingness)
Multiple R-squared:  0.2099,    Adjusted R-squared:  0.1185 
F-statistic: 2.296 on 14 and 121 DF,  p-value: 0.007933
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(MSE) ~ scale(age) + gender + scale(education_catX) + urban_rural_cat2 +  
    +(1 | target)
   Data: d_sim_us_th %>% mutate(education_catX = as.numeric(education_catX))

REML criterion at convergence: 385

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.41827 -0.67531  0.09798  0.79061  1.58834 

Random effects:
 Groups   Name        Variance Std.Dev.
 target   (Intercept) 0.1140   0.3376  
 Residual             0.8686   0.9320  
Number of obs: 136, groups:  target, 10

Fixed effects:
                        Estimate Std. Error        df t value Pr(>|t|)  
(Intercept)             -0.43240    0.25506  73.12575  -1.695   0.0943 .
scale(age)              -0.04663    0.09722 124.49344  -0.480   0.6323  
gender_m                 0.36711    0.24108 124.81442   1.523   0.1303  
gender_o                -0.85408    0.45232 126.46275  -1.888   0.0613 .
scale(education_catX)   -0.16103    0.09799 126.58868  -1.643   0.1028  
urban_rural_cat2_rural  -0.02029    0.08875 124.48547  -0.229   0.8195  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) scl(g) gndr_m gendr_ sc(_X)
scale(age)   0.012                            
gender_m    -0.770 -0.041                     
gender_o     0.843  0.008 -0.934              
scl(dctn_X) -0.022  0.510  0.001 -0.012       
urbn_rrl_2_ -0.084 -0.073  0.003  0.032  0.145
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(MSE) ~ scale(age) + gender + education_cat2 + urban_rural_cat2 +  
    +(1 | target)
   Data: d_sim_us_th

REML criterion at convergence: 385.4

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.37876 -0.64097  0.08029  0.78479  1.55234 

Random effects:
 Groups   Name        Variance Std.Dev.
 target   (Intercept) 0.1104   0.3323  
 Residual             0.8737   0.9347  
Number of obs: 136, groups:  target, 10

Fixed effects:
                        Estimate Std. Error        df t value Pr(>|t|)  
(Intercept)             -0.41713    0.25542  74.51282  -1.633   0.1067  
scale(age)              -0.04813    0.10121 125.34264  -0.476   0.6352  
gender_m                 0.37575    0.24180 124.85578   1.554   0.1227  
gender_o                -0.88541    0.45370 126.47088  -1.952   0.0532 .
education_cat2_coll     -0.15386    0.10501 128.01323  -1.465   0.1453  
urban_rural_cat2_rural  -0.03009    0.09054 125.28634  -0.332   0.7402  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) scl(g) gndr_m gendr_ edc_2_
scale(age)  -0.015                            
gender_m    -0.769 -0.053                     
gender_o     0.841  0.032 -0.934              
edctn_ct2_c -0.067  0.560 -0.024  0.033       
urbn_rrl_2_ -0.095 -0.008 -0.003  0.041  0.233

Call:
betareg(formula = MSE_rescaled ~ scale(age) + gender + scale(education_catX) + urban_rural_cat2 + 
    target, data = d_sim_us_th %>% mutate(education_catX = as.numeric(education_catX)))

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.5198 -0.5897  0.1544  0.8069  1.7317 

Coefficients (mean model with logit link):
                       Estimate Std. Error z value Pr(>|z|)    
(Intercept)            -0.96827    0.18469  -5.243 1.58e-07 ***
scale(age)             -0.04712    0.06597  -0.714   0.4751    
gender_m                0.28332    0.18933   1.496   0.1345    
gender_o               -0.58308    0.36233  -1.609   0.1076    
scale(education_catX)  -0.11774    0.06739  -1.747   0.0806 .  
urban_rural_cat2_rural -0.03029    0.06050  -0.501   0.6166    
target1                 0.24592    0.16082   1.529   0.1262    
target2                 0.05878    0.17230   0.341   0.7330    
target3                 0.34132    0.15339   2.225   0.0261 *  
target4                 0.02851    0.16243   0.175   0.8607    
target5                 0.07175    0.16613   0.432   0.6658    
target6                -0.02791    0.16013  -0.174   0.8616    
target7                -0.71032    0.17943  -3.959 7.53e-05 ***
target8                 0.25889    0.15853   1.633   0.1025    
target9                -0.13803    0.18780  -0.735   0.4624    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   10.070      1.174   8.575   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 78.85 on 16 Df
Pseudo R-squared: 0.2113
Number of iterations: 25 (BFGS) + 2 (Fisher scoring) 

Call:
betareg(formula = MSE_rescaled ~ scale(age) + gender + education_cat2 + urban_rural_cat2 + 
    target, data = d_sim_us_th %>% mutate(education_catX = as.numeric(education_catX)))

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.4810 -0.5877  0.2059  0.8117  1.7155 

Coefficients (mean model with logit link):
                       Estimate Std. Error z value Pr(>|z|)    
(Intercept)            -0.95663    0.18517  -5.166 2.39e-07 ***
scale(age)             -0.05056    0.06862  -0.737   0.4612    
gender_m                0.28897    0.18950   1.525   0.1273    
gender_o               -0.60678    0.36264  -1.673   0.0943 .  
education_cat2_coll    -0.11594    0.07221  -1.606   0.1083    
urban_rural_cat2_rural -0.03854    0.06184  -0.623   0.5331    
target1                 0.25454    0.16207   1.571   0.1163    
target2                 0.04178    0.17512   0.239   0.8114    
target3                 0.33357    0.15353   2.173   0.0298 *  
target4                 0.01577    0.16203   0.097   0.9225    
target5                 0.07896    0.16626   0.475   0.6348    
target6                -0.02272    0.16038  -0.142   0.8873    
target7                -0.70516    0.17996  -3.918 8.91e-05 ***
target8                 0.25549    0.15877   1.609   0.1076    
target9                -0.12780    0.18777  -0.681   0.4961    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)    10.03       1.17   8.576   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 78.59 on 16 Df
Pseudo R-squared: 0.2078
Number of iterations: 25 (BFGS) + 2 (Fisher scoring) 

China

Age


Call:
lm(formula = scale(MSE) ~ scale(age), data = d_sim_us_ch)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.6401 -0.9270  0.2186  0.9429  1.4894 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)
(Intercept) -8.630e-17  8.582e-02   0.000    1.000
scale(age)  -7.607e-02  8.614e-02  -0.883    0.379

Residual standard error: 1.001 on 134 degrees of freedom
Multiple R-squared:  0.005787,  Adjusted R-squared:  -0.001633 
F-statistic: 0.7799 on 1 and 134 DF,  p-value: 0.3788

Call:
betareg(formula = MSE_rescaled ~ scale(age), data = d_sim_us_ch)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.9881 -0.7190  0.3622  0.8740  1.2973 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.76385    0.06789 -11.251   <2e-16 ***
scale(age)  -0.06363    0.06659  -0.956    0.339    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)    6.260      0.716   8.743   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 54.15 on 3 Df
Pseudo R-squared: 0.007092
Number of iterations: 14 (BFGS) + 1 (Fisher scoring) 

Gender


Call:
lm(formula = scale(MSE) ~ gender, data = d_sim_us_ch)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.6141 -0.9148  0.1855  0.9476  1.4303 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)  0.01251    0.08633   0.145    0.885
gender_m     0.06822    0.08633   0.790    0.431

Residual standard error: 1.001 on 133 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.004673,  Adjusted R-squared:  -0.00281 
F-statistic: 0.6245 on 1 and 133 DF,  p-value: 0.4308

Call:
betareg(formula = MSE_rescaled ~ gender, data = d_sim_us_ch)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.9488 -0.7116  0.3493  0.8853  1.2490 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.75439    0.06819 -11.064   <2e-16 ***
gender_m     0.06240    0.06649   0.938    0.348    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   6.2494     0.7172   8.714   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 53.45 on 3 Df
Pseudo R-squared: 0.006282
Number of iterations: 13 (BFGS) + 1 (Fisher scoring) 

Race/ethnicity

Education


Call:
lm(formula = scale(MSE) ~ scale(education_catX), data = d_sim_us_ch %>% 
    mutate(education_catX = as.numeric(education_catX)))

Residuals:
    Min      1Q  Median      3Q     Max 
-1.5596 -0.8794  0.1408  0.9548  1.3884 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)
(Intercept)           -0.000251   0.086838  -0.003    0.998
scale(education_catX) -0.018471   0.087164  -0.212    0.832

Residual standard error: 1.005 on 132 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.0003401, Adjusted R-squared:  -0.007233 
F-statistic: 0.04491 on 1 and 132 DF,  p-value: 0.8325

Call:
betareg(formula = MSE_rescaled ~ scale(education_catX), data = d_sim_us_ch %>% mutate(education_catX = as.numeric(education_catX)))

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.8521 -0.6530  0.3050  0.8965  1.2015 

Coefficients (mean model with logit link):
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)           -0.76389    0.06871 -11.118   <2e-16 ***
scale(education_catX) -0.01453    0.06715  -0.216    0.829    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   6.1868     0.7124   8.685   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood:  52.7 on 3 Df
Pseudo R-squared: 0.0003308
Number of iterations: 12 (BFGS) + 2 (Fisher scoring) 


Call:
lm(formula = scale(MSE) ~ education_cat2, data = d_sim_us_ch)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.5988 -0.8369  0.1694  0.9355  1.4230 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)          0.007521   0.087503   0.086    0.932
education_cat2_coll -0.057860   0.087503  -0.661    0.510

Residual standard error: 1.004 on 132 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared:  0.003301,  Adjusted R-squared:  -0.004249 
F-statistic: 0.4372 on 1 and 132 DF,  p-value: 0.5096

Call:
betareg(formula = MSE_rescaled ~ education_cat2, data = d_sim_us_ch)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.9182 -0.5995  0.3323  0.8721  1.2363 

Coefficients (mean model with logit link):
                    Estimate Std. Error z value Pr(>|z|)    
(Intercept)         -0.75733    0.06907 -10.965   <2e-16 ***
education_cat2_coll -0.05171    0.06735  -0.768    0.443    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   6.2123     0.7155   8.682   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 52.97 on 3 Df
Pseudo R-squared: 0.004247
Number of iterations: 13 (BFGS) + 2 (Fisher scoring) 

Rural/urban


Call:
lm(formula = scale(MSE) ~ urban_rural_cat2, data = d_sim_us_ch)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.6540 -1.0079  0.1715  0.8804  1.4564 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)
(Intercept)            -0.01616    0.08672  -0.186    0.852
urban_rural_cat2_rural -0.13678    0.08672  -1.577    0.117

Residual standard error: 0.9975 on 133 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.01836,   Adjusted R-squared:  0.01098 
F-statistic: 2.488 on 1 and 133 DF,  p-value: 0.1171

Call:
betareg(formula = MSE_rescaled ~ urban_rural_cat2, data = d_sim_us_ch)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.9699 -0.7943  0.3373  0.8410  1.2550 

Coefficients (mean model with logit link):
                       Estimate Std. Error z value Pr(>|z|)    
(Intercept)            -0.77628    0.06903  -11.24   <2e-16 ***
urban_rural_cat2_rural -0.09550    0.06727   -1.42    0.156    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   6.2690     0.7198    8.71   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood:  53.9 on 3 Df
Pseudo R-squared: 0.01448
Number of iterations: 14 (BFGS) + 2 (Fisher scoring) 

Religion


Call:
lm(formula = scale(MSE) ~ religion_cat3, data = d_sim_us_ch)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7062 -0.8712  0.1571  0.9001  1.3836 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)
(Intercept)             0.06732    0.13503   0.499    0.619
religion_cat3_buddhist -0.07633    0.18454  -0.414    0.680
religion_cat3_other     0.10542    0.23106   0.456    0.649

Residual standard error: 0.9743 on 111 degrees of freedom
  (22 observations deleted due to missingness)
Multiple R-squared:  0.001959,  Adjusted R-squared:  -0.01602 
F-statistic: 0.1089 on 2 and 111 DF,  p-value: 0.8969

Call:
betareg(formula = MSE_rescaled ~ religion_cat3, data = d_sim_us_ch)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.2177 -0.6896  0.3119  0.8983  1.2332 

Coefficients (mean model with logit link):
                       Estimate Std. Error z value Pr(>|z|)    
(Intercept)            -0.71150    0.10376  -6.857 7.02e-12 ***
religion_cat3_buddhist -0.02217    0.14076  -0.158    0.875    
religion_cat3_other     0.04743    0.17554   0.270    0.787    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   6.7684     0.8474   7.987 1.38e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 47.34 on 4 Df
Pseudo R-squared: 0.0006548
Number of iterations: 15 (BFGS) + 2 (Fisher scoring) 

Target


Call:
lm(formula = scale(MSE) ~ target, data = d_sim_us_ch, contrasts = list(target = "contr.sum"))

Residuals:
    Min      1Q  Median      3Q     Max 
-1.6974 -0.8158  0.1577  0.7392  2.0600 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)   
(Intercept) -0.001253   0.082341  -0.015  0.98788   
target1     -0.464532   0.243725  -1.906  0.05893 . 
target2      0.165268   0.243725   0.678  0.49896   
target3      0.387510   0.243725   1.590  0.11435   
target4      0.464226   0.251892   1.843  0.06769 . 
target5     -0.298024   0.243725  -1.223  0.22369   
target6     -0.053026   0.243725  -0.218  0.82812   
target7     -0.718161   0.251892  -2.851  0.00509 **
target8      0.048520   0.251892   0.193  0.84756   
target9      0.433270   0.243725   1.778  0.07786 . 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9596 on 126 degrees of freedom
Multiple R-squared:  0.1405,    Adjusted R-squared:  0.07912 
F-statistic: 2.289 on 9 and 126 DF,  p-value: 0.02056

Call:
betareg(formula = MSE_rescaled ~ target, data = d_sim_us_ch)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.2183 -0.8881  0.2734  0.7980  1.7910 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.77881    0.06425 -12.121  < 2e-16 ***
target1     -0.39628    0.19423  -2.040  0.04132 *  
target2      0.08056    0.18291   0.440  0.65961    
target3      0.33391    0.17918   1.864  0.06238 .  
target4      0.39364    0.18446   2.134  0.03284 *  
target5     -0.23870    0.18993  -1.257  0.20882    
target6     -0.01486    0.18475  -0.080  0.93591    
target7     -0.55420    0.20576  -2.693  0.00707 ** 
target8      0.02459    0.19011   0.129  0.89708    
target9      0.37492    0.17874   2.098  0.03594 *  

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   7.3215     0.8455   8.659   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 64.59 on 11 Df
Pseudo R-squared: 0.146
Number of iterations: 22 (BFGS) + 1 (Fisher scoring) 

All together


Call:
lm(formula = scale(MSE) ~ scale(age) + gender + scale(education_catX) + 
    religion_cat3 + urban_rural_cat2 + target, data = d_sim_us_ch %>% 
    mutate(education_catX = as.numeric(education_catX)))

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7855 -0.8826  0.2065  0.7311  1.6315 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)
(Intercept)             0.028043   0.146589   0.191    0.849
scale(age)              0.035009   0.115519   0.303    0.763
gender_m               -0.036314   0.101044  -0.359    0.720
scale(education_catX)  -0.015559   0.115801  -0.134    0.893
religion_cat3_buddhist  0.008771   0.196799   0.045    0.965
religion_cat3_other     0.002453   0.250625   0.010    0.992
urban_rural_cat2_rural -0.153559   0.106015  -1.448    0.151
target1                -0.408868   0.275045  -1.487    0.140
target2                -0.064240   0.333506  -0.193    0.848
target3                 0.301786   0.257671   1.171    0.244
target4                 0.431153   0.283818   1.519    0.132
target5                -0.249005   0.290031  -0.859    0.393
target6                -0.074439   0.260780  -0.285    0.776
target7                -0.438204   0.313731  -1.397    0.166
target8                 0.007805   0.270807   0.029    0.977
target9                 0.463229   0.291097   1.591    0.115

Residual standard error: 0.978 on 95 degrees of freedom
  (25 observations deleted due to missingness)
Multiple R-squared:  0.1156,    Adjusted R-squared:  -0.024 
F-statistic: 0.8281 on 15 and 95 DF,  p-value: 0.6444

Call:
lm(formula = scale(MSE) ~ scale(age) + gender + education_cat2 + 
    religion_cat3 + urban_rural_cat2 + target, data = d_sim_us_ch)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.8059 -0.8842  0.1773  0.7210  1.6292 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)
(Intercept)             0.02903    0.14333   0.203    0.840
scale(age)              0.02406    0.11500   0.209    0.835
gender_m               -0.03707    0.10088  -0.367    0.714
education_cat2_coll    -0.04142    0.11263  -0.368    0.714
religion_cat3_buddhist  0.01295    0.19615   0.066    0.947
religion_cat3_other    -0.00583    0.24813  -0.023    0.981
urban_rural_cat2_rural -0.15952    0.10522  -1.516    0.133
target1                -0.40541    0.27443  -1.477    0.143
target2                -0.06736    0.33199  -0.203    0.840
target3                 0.29438    0.25825   1.140    0.257
target4                 0.43606    0.28401   1.535    0.128
target5                -0.25177    0.28941  -0.870    0.387
target6                -0.07225    0.25994  -0.278    0.782
target7                -0.43983    0.31339  -1.403    0.164
target8                 0.01196    0.26895   0.044    0.965
target9                 0.45775    0.29120   1.572    0.119

Residual standard error: 0.9774 on 95 degrees of freedom
  (25 observations deleted due to missingness)
Multiple R-squared:  0.1167,    Adjusted R-squared:  -0.02274 
F-statistic: 0.837 on 15 and 95 DF,  p-value: 0.6348
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(MSE) ~ scale(age) + gender + scale(education_catX) + religion_cat3 +  
    urban_rural_cat2 + +(1 | target)
   Data: d_sim_us_ch %>% mutate(education_catX = as.numeric(education_catX))

REML criterion at convergence: 321.6

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.8142 -0.7211  0.1416  0.8107  1.3285 

Random effects:
 Groups   Name        Variance Std.Dev.
 target   (Intercept) 0.0143   0.1196  
 Residual             0.9543   0.9769  
Number of obs: 111, groups:  target, 10

Fixed effects:
                        Estimate Std. Error        df t value Pr(>|t|)
(Intercept)              0.04948    0.14606  28.34635   0.339    0.737
scale(age)               0.01140    0.11235 102.08469   0.101    0.919
gender_m                 0.01161    0.09482 101.28006   0.122    0.903
scale(education_catX)   -0.02298    0.11315 101.62214  -0.203    0.839
religion_cat3_buddhist  -0.01219    0.19375  99.54632  -0.063    0.950
religion_cat3_other      0.03500    0.24076 103.80214   0.145    0.885
urban_rural_cat2_rural  -0.14400    0.10106 103.99762  -1.425    0.157

Correlation of Fixed Effects:
            (Intr) scl(g) gndr_m sc(_X) rlgn_ct3_b rlgn_ct3_t
scale(age)   0.063                                           
gender_m     0.032  0.114                                    
scl(dctn_X)  0.179  0.459 -0.008                             
rlgn_ct3_bd -0.104 -0.194 -0.020 -0.114                      
rlgn_ct3_th  0.533  0.150  0.018  0.186 -0.772               
urbn_rrl_2_  0.175 -0.088 -0.083  0.253 -0.113      0.166    
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(MSE) ~ scale(age) + gender + education_cat2 + religion_cat3 +  
    urban_rural_cat2 + +(1 | target)
   Data: d_sim_us_ch

REML criterion at convergence: 321.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.8524 -0.6818  0.1489  0.8165  1.3586 

Random effects:
 Groups   Name        Variance Std.Dev.
 target   (Intercept) 0.0131   0.1144  
 Residual             0.9535   0.9765  
Number of obs: 111, groups:  target, 10

Fixed effects:
                         Estimate Std. Error         df t value Pr(>|t|)
(Intercept)              0.052293   0.143287  27.779170   0.365    0.718
scale(age)              -0.001931   0.111189 102.952528  -0.017    0.986
gender_m                 0.010737   0.094737 101.292340   0.113    0.910
education_cat2_coll     -0.053152   0.110796 100.336817  -0.480    0.632
religion_cat3_buddhist  -0.008157   0.193233  99.409248  -0.042    0.966
religion_cat3_other      0.027370   0.239003 103.643618   0.115    0.909
urban_rural_cat2_rural  -0.150318   0.100632 103.949083  -1.494    0.138

Correlation of Fixed Effects:
            (Intr) scl(g) gndr_m edc_2_ rlgn_ct3_b rlgn_ct3_t
scale(age)  -0.002                                           
gender_m     0.035  0.130                                    
edctn_ct2_c  0.038  0.442  0.027                             
rlgn_ct3_bd -0.089 -0.184 -0.024 -0.094                      
rlgn_ct3_th  0.518  0.131  0.024  0.147 -0.771               
urbn_rrl_2_  0.142 -0.101 -0.075  0.240 -0.107      0.155    

Call:
betareg(formula = MSE_rescaled ~ scale(age) + gender + scale(education_catX) + religion_cat3 + 
    urban_rural_cat2 + target, data = d_sim_us_ch %>% mutate(education_catX = as.numeric(education_catX)))

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.5453 -0.8965  0.3746  0.8230  1.7245 

Coefficients (mean model with logit link):
                        Estimate Std. Error z value Pr(>|z|)    
(Intercept)            -0.758869   0.107908  -7.033 2.03e-12 ***
scale(age)              0.041071   0.083932   0.489   0.6246    
gender_m               -0.013005   0.073502  -0.177   0.8596    
scale(education_catX)  -0.002331   0.084566  -0.028   0.9780    
religion_cat3_buddhist  0.029752   0.142956   0.208   0.8351    
religion_cat3_other    -0.029668   0.182169  -0.163   0.8706    
urban_rural_cat2_rural -0.106610   0.077490  -1.376   0.1689    
target1                -0.361979   0.208487  -1.736   0.0825 .  
target2                -0.078838   0.243428  -0.324   0.7460    
target3                 0.264616   0.182843   1.447   0.1478    
target4                 0.354677   0.200512   1.769   0.0769 .  
target5                -0.160546   0.213606  -0.752   0.4523    
target6                -0.038818   0.190519  -0.204   0.8385    
target7                -0.309536   0.237075  -1.306   0.1917    
target8                -0.011186   0.197162  -0.057   0.9548    
target9                 0.380669   0.206562   1.843   0.0653 .  

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   7.6468     0.9771   7.826 5.04e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 52.62 on 17 Df
Pseudo R-squared: 0.1129
Number of iterations: 28 (BFGS) + 2 (Fisher scoring) 

Call:
betareg(formula = MSE_rescaled ~ scale(age) + gender + education_cat2 + religion_cat3 + 
    urban_rural_cat2 + target, data = d_sim_us_ch %>% mutate(education_catX = as.numeric(education_catX)))

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.5479 -0.8757  0.3207  0.8299  1.7071 

Coefficients (mean model with logit link):
                        Estimate Std. Error z value Pr(>|z|)    
(Intercept)            -0.759947   0.105693  -7.190 6.47e-13 ***
scale(age)              0.028921   0.083692   0.346   0.7297    
gender_m               -0.013364   0.073422  -0.182   0.8556    
education_cat2_coll    -0.029635   0.082116  -0.361   0.7182    
religion_cat3_buddhist  0.034925   0.142486   0.245   0.8064    
religion_cat3_other    -0.039434   0.180433  -0.219   0.8270    
urban_rural_cat2_rural -0.112401   0.077024  -1.459   0.1445    
target1                -0.357477   0.208019  -1.718   0.0857 .  
target2                -0.082636   0.242370  -0.341   0.7331    
target3                 0.257171   0.183360   1.403   0.1608    
target4                 0.357698   0.200704   1.782   0.0747 .  
target5                -0.161388   0.213190  -0.757   0.4490    
target6                -0.039133   0.189964  -0.206   0.8368    
target7                -0.309594   0.236850  -1.307   0.1912    
target8                -0.006849   0.195916  -0.035   0.9721    
target9                 0.376658   0.206667   1.823   0.0684 .  

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   7.6547     0.9782   7.826 5.05e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 52.69 on 17 Df
Pseudo R-squared: 0.1145
Number of iterations: 29 (BFGS) + 2 (Fisher scoring) 

Vanuatu

Age


Call:
lm(formula = scale(MSE) ~ scale(age), data = d_sim_us_vt)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.3050 -1.0483 -0.1352  1.0298  1.4923 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept)  0.001327   0.084256   0.016    0.987
scale(age)  -0.035285   0.084554  -0.417    0.677

Residual standard error: 1.004 on 140 degrees of freedom
  (6 observations deleted due to missingness)
Multiple R-squared:  0.001242,  Adjusted R-squared:  -0.005892 
F-statistic: 0.1741 on 1 and 140 DF,  p-value: 0.6771

Call:
betareg(formula = MSE_rescaled ~ scale(age), data = d_sim_us_vt)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.6437 -1.0030  0.1053  0.9601  1.2703 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.92336    0.07184 -12.853   <2e-16 ***
scale(age)  -0.02107    0.06920  -0.304    0.761    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   5.5929     0.6277    8.91   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood:  59.1 on 3 Df
Pseudo R-squared: 0.0007607
Number of iterations: 13 (BFGS) + 1 (Fisher scoring) 

Gender


Call:
lm(formula = scale(MSE) ~ gender, data = d_sim_us_vt)

Residuals:
   Min     1Q Median     3Q    Max 
-1.337 -1.047 -0.132  1.015  1.490 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.003007   0.089103   0.034    0.973
gender_m    0.007947   0.089103   0.089    0.929

Residual standard error: 1.003 on 146 degrees of freedom
Multiple R-squared:  5.448e-05, Adjusted R-squared:  -0.006794 
F-statistic: 0.007954 on 1 and 146 DF,  p-value: 0.9291

Call:
betareg(formula = MSE_rescaled ~ gender, data = d_sim_us_vt)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-1.6613 -0.9914  0.1019  0.9484  1.2625 

Coefficients (mean model with logit link):
             Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.926602   0.075681 -12.244   <2e-16 ***
gender_m    -0.004612   0.072886  -0.063     0.95    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   5.5831     0.6138   9.096   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 61.59 on 3 Df
Pseudo R-squared: 2.835e-05
Number of iterations: 13 (BFGS) + 2 (Fisher scoring) 

Location


Call:
lm(formula = scale(MSE) ~ location_cat2, data = d_sim_us_vt)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.5562 -1.0115 -0.0272  0.8939  1.7147 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)   
(Intercept)         -3.650e-17  8.026e-02   0.000  1.00000   
location_cat2_urban  2.298e-01  8.026e-02   2.863  0.00481 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9764 on 146 degrees of freedom
Multiple R-squared:  0.05317,   Adjusted R-squared:  0.04668 
F-statistic: 8.198 on 1 and 146 DF,  p-value: 0.00481

Call:
betareg(formula = MSE_rescaled ~ location_cat2, data = d_sim_us_vt)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-2.0087 -1.1682  0.1931  0.8879  1.4244 

Coefficients (mean model with logit link):
                    Estimate Std. Error z value Pr(>|z|)    
(Intercept)         -0.92996    0.06943 -13.393   <2e-16 ***
location_cat2_urban  0.16632    0.06666   2.495   0.0126 *  

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   5.8260     0.6423    9.07   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 64.65 on 3 Df
Pseudo R-squared: 0.04225
Number of iterations: 13 (BFGS) + 2 (Fisher scoring) 

Target


Call:
lm(formula = scale(MSE) ~ target, data = d_sim_us_vt, contrasts = list(target = "contr.sum"))

Residuals:
    Min      1Q  Median      3Q     Max 
-2.1635 -0.7050 -0.1090  0.7198  1.8083 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.03220    0.08829   0.365   0.7159    
target1     -0.48900    0.22617  -2.162   0.0323 *  
target2      0.28486    0.22617   1.260   0.2100    
target3      0.12905    0.24047   0.537   0.5924    
target4      0.31075    0.57705   0.539   0.5911    
target5      0.12944    0.23292   0.556   0.5793    
target6     -0.14580    0.22617  -0.645   0.5202    
target7     -0.31136    0.22617  -1.377   0.1709    
target8     -1.08023    0.22617  -4.776 4.54e-06 ***
target9      0.33796    0.23292   1.451   0.1491    
target10    -0.05567    0.22617  -0.246   0.8059    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8916 on 137 degrees of freedom
Multiple R-squared:  0.2592,    Adjusted R-squared:  0.2051 
F-statistic: 4.793 on 10 and 137 DF,  p-value: 6.744e-06

Call:
betareg(formula = MSE_rescaled ~ target, data = d_sim_us_vt)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.1002 -0.6754  0.0639  0.8512  1.7259 

Coefficients (mean model with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.91934    0.07430 -12.374  < 2e-16 ***
target1     -0.45633    0.19901  -2.293   0.0218 *  
target2      0.16604    0.18352   0.905   0.3656    
target3      0.06323    0.19725   0.321   0.7485    
target4      0.37066    0.45753   0.810   0.4179    
target5      0.09951    0.19029   0.523   0.6010    
target6     -0.08487    0.18883  -0.449   0.6531    
target7     -0.18554    0.19134  -0.970   0.3322    
target8     -0.92474    0.21434  -4.314  1.6e-05 ***
target9      0.26936    0.18714   1.439   0.1500    
target10    -0.01217    0.18714  -0.065   0.9482    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   7.3600     0.8227   8.947   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 81.51 on 12 Df
Pseudo R-squared: 0.2574
Number of iterations: 21 (BFGS) + 1 (Fisher scoring) 

All together


Call:
lm(formula = scale(MSE) ~ scale(age) + gender + location_cat2 + 
    target, data = d_sim_us_vt)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.37496 -0.57032 -0.03288  0.65019  2.01186 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)          0.06735    0.09407   0.716  0.47531    
scale(age)          -0.05112    0.07494  -0.682  0.49637    
gender_m             0.02968    0.08134   0.365  0.71576    
location_cat2_urban  0.20932    0.07438   2.814  0.00567 ** 
target1             -0.53369    0.22410  -2.381  0.01872 *  
target2              0.33815    0.23033   1.468  0.14452    
target3              0.08882    0.23698   0.375  0.70844    
target4              0.56692    0.57601   0.984  0.32687    
target5              0.09823    0.22937   0.428  0.66918    
target6             -0.26657    0.23636  -1.128  0.26151    
target7             -0.31378    0.22388  -1.402  0.16347    
target8             -1.07607    0.23039  -4.671 7.48e-06 ***
target9              0.34600    0.22895   1.511  0.13319    
target10            -0.09678    0.23167  -0.418  0.67684    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8739 on 128 degrees of freedom
  (6 observations deleted due to missingness)
Multiple R-squared:  0.3082,    Adjusted R-squared:  0.2379 
F-statistic: 4.386 on 13 and 128 DF,  p-value: 4.248e-06
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: scale(MSE) ~ scale(age) + gender + location_cat2 + +(1 | target)
   Data: d_sim_us_vt

REML criterion at convergence: 389

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.51911 -0.74027 -0.02537  0.83385  2.19707 

Random effects:
 Groups   Name        Variance Std.Dev.
 target   (Intercept) 0.2248   0.4741  
 Residual             0.7624   0.8731  
Number of obs: 142, groups:  target, 11

Fixed effects:
                     Estimate Std. Error        df t value Pr(>|t|)   
(Intercept)           0.03352    0.16620  10.50053   0.202  0.84400   
scale(age)           -0.04136    0.07444 130.20734  -0.556  0.57940   
gender_m              0.02055    0.08058 131.89639   0.255  0.79906   
location_cat2_urban   0.20776    0.07397 129.87567   2.809  0.00575 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) scl(g) gndr_m
scale(age)  -0.024              
gender_m     0.181 -0.065       
lctn_ct2_rb  0.013  0.067 -0.032

Call:
betareg(formula = MSE_rescaled ~ scale(age) + gender + location_cat2 + target, data = d_sim_us_vt)

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max 
-3.5086 -0.5677  0.0677  0.8122  1.8919 

Coefficients (mean model with logit link):
                    Estimate Std. Error z value Pr(>|z|)    
(Intercept)         -0.89618    0.07848 -11.420  < 2e-16 ***
scale(age)          -0.03253    0.06276  -0.518  0.60423    
gender_m             0.02976    0.06773   0.439  0.66034    
location_cat2_urban  0.16971    0.06215   2.731  0.00632 ** 
target1             -0.51796    0.19745  -2.623  0.00871 ** 
target2              0.23587    0.18440   1.279  0.20086    
target3              0.03755    0.19377   0.194  0.84633    
target4              0.57194    0.45596   1.254  0.20972    
target5              0.08506    0.18665   0.456  0.64859    
target6             -0.19547    0.20002  -0.977  0.32845    
target7             -0.18048    0.18889  -0.955  0.33934    
target8             -0.94714    0.21899  -4.325 1.53e-05 ***
target9              0.29123    0.18322   1.589  0.11195    
target10            -0.04096    0.19104  -0.214  0.83022    

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)    
(phi)   7.8236     0.8955   8.737   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Type of estimator: ML (maximum likelihood)
Log-likelihood: 82.17 on 15 Df
Pseudo R-squared: 0.2949
Number of iterations: 23 (BFGS) + 2 (Fisher scoring) 

Summary stats

Group-level average matching scores (observed)

Theoretical range for MSE

Kinda = 0.5

Kinda = yes = 1

MSE summary stats by group (observed)

Column `education_cat2` joining factors with different levels, coercing to character vectorColumn `ethnicity_cat2` joining factors with different levels, coercing to character vectorColumn `gender` joining factors with different levels, coercing to character vectorColumn `education_cat2` joining character vector and factor, coercing into character vectorColumn `ethnicity_cat2` joining character vector and factor, coercing into character vectorColumn `religion_cat3` joining factors with different levels, coercing to character vectorColumn `target` joining factors with different levels, coercing to character vectorColumn `gender` joining character vector and factor, coercing into character vectorColumn `education_cat2` joining character vector and factor, coercing into character vectorColumn `ethnicity_cat2` joining character vector and factor, coercing into character vectorColumn `religion_cat3` joining character vector and factor, coercing into character vectorColumn `target` joining character vector and factor, coercing into character vectorColumn `gender` joining character vector and factor, coercing into character vectorColumn `target` joining character vector and factor, coercing into character vectorColumn `gender` joining character vector and factor, coercing into character vectorColumn `ethnicity_cat2` joining character vector and factor, coercing into character vectorColumn `religion_cat3` joining character vector and factor, coercing into character vectorColumn `target` joining character vector and factor, coercing into character vectorColumn `gender` joining character vector and factor, coercing into character vectorColumn `religion_cat3` joining character vector and factor, coercing into character vectorColumn `target` joining character vector and factor, coercing into character vectorColumn `gender` joining character vector and factor, coercing into character vectorColumn `religion_cat3` joining character vector and factor, coercing into character vectorColumn `target` joining character vector and factor, coercing into character vectorColumn `gender` joining character vector and factor, coercing into character vectorColumn `target` joining character vector and factor, coercing into character vectorColumn `gender` joining character vector and factor, coercing into character vectorColumn `location_cat2` has different attributes on LHS and RHS of join
---
title: "Concepts of mental life across cultures: Fit between individuals and cultural modesl"
authors: "Weisman, Legare, & Luhrmann"
output: 
  html_notebook:
    toc: true
    toc_float: true
---

```{r setup}
knitr::opts_chunk$set(echo = F, message = F)
```

```{r}
library(betareg)
library(lme4)
library(lmerTest)
```

In this notebook we conduct exploratory factor analyses (EFAs) on the datasets for our studies of concepts of mental life, in which each participants judged the various mental capacities of a particular target entity. We analyze datasets for adults and children from each of our five field sites: the Ghana, Ghana, Thailand, China, and Vanuatu. 
      
This notebook contains an exploration of how well the cultural model represented by the EFA solution describes the responses of individuals within that culture, and whether this "fit" between individtual and cultural model varies along demographic lines.

**NOTE: As of now, the "efa_oblique.Rmd" notebook (or one of the alternative versions of these analyses) must be run prior to this notebook.** 

```{r}
# read in participant logs for full demographics
plog_us_adults <- read_csv("../demographics/plog_us_adults.csv") %>%
  mutate(gender = factor(gender, 
                         levels = c("F", "M"),
                         labels = c("female", "male")),
         education_catX = case_when(
           education_cat %in% c("less than high school", "some high school") ~ "less than high school degree", 
           education_cat %in% c("ged", "high school") ~ "high school degree",
           education_cat %in% c("trade/certificate") ~ "trade/certificate",
           education_cat %in% c("associates", "some college") ~ "some college",
           education_cat %in% c("bachelors", "other college degree") ~ "college degree",
           education_cat %in% c("masters", "some graduate school") ~ "masters",
           education_cat %in% c("phd", "professional degree") ~ "profesional degree"),
         education_catX = factor(education_catX,
                                 levels = c("less than high school degree", "high school degree",
                                            "trade/certificate", "some college", "college degree",
                                            "masters", "professional degree")),
         education_cat2 = case_when(
           education_catX %in% c("less than high school degree", "high school degree", "trade/certificate") ~ "no college",
           education_catX %in% c("some college", "college degree", "masters", "professional degree") ~ "at least some college",
           TRUE ~ NA_character_),
         education_cat2 = factor(education_cat2, levels = c("no college", "at least some college")),
         ethnicity_cat2 = case_when(ethnicity_cat == "white" ~ "White",
                                    is.na(ethnicity_cat) ~ NA_character_,
                                    TRUE ~ "POC"),
         ethnicity_cat2 = factor(ethnicity_cat2,
                                 levels = c("White", "POC")),
         urban_rural_cat2 = case_when(urban_rural_cat == "rural" ~ "rural",
                                      is.na(urban_rural_cat) ~ NA_character_,
                                      TRUE ~ "urban (etc.)"),
         urban_rural_cat2 = factor(urban_rural_cat2,
                                   levels = c("urban (etc.)", "rural")),
         class_cat = factor(class_cat,
                            levels = c("lower/working class", "lower middle", 
                                       "middle", "upper middle", "upper")),
         class_cat2 = factor(class_cat2,
                             levels = c("lower", "middle", "upper")),
         religion_cat3 = case_when(religion_cat == "christian" ~ "Christian",
                                   religion_cat %in% c("na", "none") ~ "None",
                                   is.na(religion_cat) ~ NA_character_,
                                   TRUE ~ "Other religious"),
         religion_cat3 = factor(religion_cat3,
                                levels = c("None", "Christian", "Other religious")))

plog_gh_adults <- read_csv("../demographics/plog_gh_adults.csv") %>%
  mutate(gender = factor(gender, 
                         levels = c("f", "m"),
                         labels = c("female", "male")),
         education_catX = case_when(
           education_cat %in% c("less than high school", "some high school") ~ "less than high school degree", 
           education_cat %in% c("ged", "high school") ~ "high school degree",
           education_cat %in% c("trade/certificate") ~ "trade/certificate",
           education_cat %in% c("associates", "some college") ~ "some college",
           education_cat %in% c("bachelors", "other college degree") ~ "college degree",
           education_cat %in% c("masters", "some graduate school") ~ "masters",
           education_cat %in% c("phd", "professional degree") ~ "profesional degree"),
         education_catX = factor(education_catX,
                                 levels = c("less than high school degree", "high school degree",
                                            "trade/certificate", "some college", "college degree",
                                            "masters", "professional degree")),
         education_cat2 = case_when(
           education_catX %in% c("less than high school degree") ~ "no degree",
           education_catX %in% c("high school degree", "trade/certificate",
                                 "some college", "college degree", "masters", "professional degree") ~ "at least HS degree",
           TRUE ~ NA_character_),
         education_cat2 = factor(education_cat2, levels = c("no degree", "at least HS degree")),
         ethnicity_cat2 = case_when(ethnicity_cat == "fante" ~ "Fante",
                                    is.na(ethnicity_cat) ~ NA_character_,
                                    TRUE ~ "other"),
         ethnicity_cat2 = factor(ethnicity_cat2,
                                 levels = c("Fante", "other")),
         urban_rural_cat2 = case_when(urban_rural_cat == "rural" ~ "rural",
                                      is.na(urban_rural_cat) ~ NA_character_,
                                      TRUE ~ "urban (etc.)"),
         urban_rural_cat2 = factor(urban_rural_cat2,
                                   levels = c("urban (etc.)", "rural")),
         class_cat = factor(class_cat,
                            levels = c("lower/working class", "lower middle", 
                                       "middle", "upper middle", "upper")),
         class_cat2 = factor(class_cat2,
                             levels = c("lower", "middle", "upper")),
         religion_cat3 = case_when(religion_cat == "christian" ~ "Christian",
                                   religion_cat %in% c("na", "none") ~ "None",
                                   is.na(religion_cat) ~ NA_character_,
                                   TRUE ~ "Other religious"),
         religion_cat3 = factor(religion_cat3,
                                levels = c("None", "Christian", "Other religious")))

plog_th_adults <- read_csv("../demographics/plog_th_adults.csv") %>%
  mutate(gender = factor(gender, 
                         levels = c("F", "M", "O"),
                         labels = c("female", "male", "other")),
         education_catX = case_when(
           education_cat %in% c("less than high school", "some high school") ~ "less than high school degree", 
           education_cat %in% c("ged", "high school") ~ "high school degree",
           education_cat %in% c("trade/certificate") ~ "trade/certificate",
           education_cat %in% c("associates", "some college") ~ "some college",
           education_cat %in% c("bachelors", "other college degree") ~ "college degree",
           education_cat %in% c("masters", "some graduate school") ~ "masters",
           education_cat %in% c("phd", "professional degree") ~ "profesional degree"),
         education_catX = factor(education_catX,
                                 levels = c("less than high school degree", "high school degree",
                                            "trade/certificate", "some college", "college degree",
                                            "masters", "professional degree")),
         education_cat2 = case_when(
           education_catX %in% c("less than high school degree", "high school degree", "trade/certificate") ~ "no college",
           education_catX %in% c("some college", "college degree", "masters", "professional degree") ~ "at least some college",
           TRUE ~ NA_character_),
         education_cat2 = factor(education_cat2, levels = c("no college", "at least some college")),
         ethnicity_cat2 = case_when(ethnicity_cat == "Thai" ~ "Thai",
                                    ethnicity_cat == "Not trans" ~ NA_character_,
                                    is.na(ethnicity_cat) ~ NA_character_,
                                    TRUE ~ "other"),
         ethnicity_cat2 = factor(ethnicity_cat2,
                                 levels = c("White", "POC")),
         urban_rural_cat2 = case_when(urban_rural_cat == "Rural" ~ "rural",
                                      urban_rural_cat == "Urban" ~ "urban (etc.)",
                                      TRUE ~ NA_character_),
         urban_rural_cat2 = factor(urban_rural_cat2,
                                   levels = c("urban (etc.)", "rural")),
         class_cat = factor(class_cat,
                            levels = c("lower/working class", "lower middle", 
                                       "middle", "upper middle", "upper")),
         class_cat2 = factor(class_cat2,
                             levels = c("lower", "middle", "upper")),
         religion_cat3 = case_when(religion_cat == "buddhist" ~ "Buddhist",
                                   religion_cat %in% c("no religion") ~ "None",
                                   is.na(religion_cat) ~ NA_character_,
                                   TRUE ~ "Other religious"),
         religion_cat3 = factor(religion_cat3,
                                levels = c("None", "Buddhist", "Other religious")))

plog_ch_adults <- read_csv("../demographics/plog_ch_adults.csv") %>%
  mutate(gender = factor(gender, 
                         levels = c("Female", "Male"),
                         labels = c("female", "male")),
         education_catX = case_when(
           education_cat %in% c("less than high school", "some high school") ~ "less than high school degree", 
           education_cat %in% c("ged", "high school") ~ "high school degree",
           education_cat %in% c("trade/certificate") ~ "trade/certificate",
           education_cat %in% c("associates", "some college") ~ "some college",
           education_cat %in% c("bachelors", "other college degree") ~ "college degree",
           education_cat %in% c("masters", "some graduate school") ~ "masters",
           education_cat %in% c("phd", "professional degree") ~ "profesional degree"),
         education_catX = factor(education_catX,
                                 levels = c("less than high school degree", "high school degree",
                                            "trade/certificate", "some college", "college degree",
                                            "masters", "professional degree")),
         education_cat2 = case_when(
           education_catX %in% c("less than high school degree", "high school degree", "trade/certificate") ~ "no college",
           education_catX %in% c("some college", "college degree", "masters", "professional degree") ~ "at least some college",
           TRUE ~ NA_character_),
         education_cat2 = factor(education_cat2, levels = c("no college", "at least some college")),
         ethnicity_cat2 = case_when(ethnicity_cat == "han" ~ "Han",
                                    is.na(ethnicity_cat) ~ NA_character_,
                                    TRUE ~ "Other"),
         ethnicity_cat2 = factor(ethnicity_cat2,
                                 levels = c("Han", "Other")),
         urban_rural_cat2 = case_when(urban_rural_cat == "rural" ~ "rural",
                                      is.na(urban_rural_cat) ~ NA_character_,
                                      TRUE ~ "urban (etc.)"),
         urban_rural_cat2 = factor(urban_rural_cat2,
                                   levels = c("urban (etc.)", "rural")),
         class_cat = factor(class_cat,
                            levels = c("lower/working class", "lower middle", 
                                       "middle", "upper middle", "upper")),
         class_cat2 = factor(class_cat2,
                             levels = c("lower", "middle", "upper")),
         religion_cat3 = case_when(religion_cat == "buddhist" ~ "Buddhist",
                                   religion_cat %in% c("na", "none", "communist party") ~ "None",
                                   is.na(religion_cat) ~ NA_character_,
                                   TRUE ~ "Other religious"),
         religion_cat3 = factor(religion_cat3,
                                levels = c("None", "Buddhist", "Other religious")))

plog_vt_adults <- read_csv("../demographics/plog_vt_adults.csv") %>%
  mutate(gender = factor(gender, 
                         levels = c("F", "M"),
                         labels = c("female", "male")))
```

```{r}
plog_us_children <- read_csv("../demographics/plog_us_children.csv") %>%
  mutate(gender = factor(gender, 
                         levels = c("F", "M"),
                         labels = c("female", "male")),
         ethnicity_cat2 = case_when(grepl("white", ethnicity_cat) |
                                      ethnicity_cat == "irish" ~ "White",
                                    is.na(ethnicity_cat) ~ NA_character_,
                                    TRUE ~ "POC"),
         ethnicity_cat2 = factor(ethnicity_cat2,
                                 levels = c("White", "POC")),
         religion_cat3 = case_when(
           religion_cat == "christian" | grepl("lutheran", religion_cat) | 
             religion_cat %in% c("presbyterian", "protestant") ~ "Christian",
           religion_cat %in% c("na", "none", "agnostic", "atheist", "healthy", 
                               "limited", "not religious", "secular") ~ "None",
           is.na(religion_cat) | religion_cat == "i don't know" ~ NA_character_,
           TRUE ~ "Other religious"),
         religion_cat3 = factor(religion_cat3,
                                levels = c("None", "Christian", "Other religious")))

plog_gh_children <- read_csv("../demographics/plog_gh_children.csv") %>%
  mutate(gender = factor(gender, 
                         levels = c("F", "M"),
                         labels = c("female", "male")),
         religion_cat3 = case_when(
           religion_cat == "christian" | grepl("lutheran", religion_cat) | 
             religion_cat %in% c("presbyterian", "protestant") ~ "Christian",
           religion_cat %in% c("na", "none", "agnostic", "atheist", "healthy", 
                               "limited", "not religious", "secular") ~ "None",
           is.na(religion_cat) | religion_cat == "i don't know" ~ NA_character_,
           TRUE ~ "Other religious"),
         religion_cat3 = factor(religion_cat3,
                                levels = c("None", "Christian", "Other religious")))

plog_th_children <- read_csv("../demographics/plog_th_children.csv") %>%
  mutate(gender = factor(gender, 
                         levels = c("F", "M"),
                         labels = c("female", "male")),
         religion_cat3 = case_when(
           religion_cat == "buddhist" ~ "Buddhist",
           religion_cat %in% c("no religion") ~ "None",
           is.na(religion_cat) | grepl("don't know", religion_cat) |
             religion_cat %in% c("maybe") ~ NA_character_,
           TRUE ~ "Other religious"),
         religion_cat3 = factor(religion_cat3,
                                levels = c("None", "Buddhist", "Other religious")))

plog_ch_children <- read_csv("../demographics/plog_ch_children.csv") %>%
  mutate(gender = factor(gender, 
                         levels = c("Female", "Male"),
                         labels = c("female", "male")))

plog_vt_children <- read_csv("../demographics/plog_vt_children.csv") %>%
  mutate(gender = factor(gender, 
                         levels = c("F", "M"),
                         labels = c("female", "male")))
```


# Functions

```{r}
# function for getting individual model ("matching")
mod_indiv_fun <- function(df_w, subj, kinda = 0.5, long = T) {
  
  df_w <- df_w %>%
    mutate_if(is.numeric, 
              ~ case_when(. == 0.5 ~ kinda,
                          TRUE ~ .))
  
  subj_responses <- df_w[subj,]
  res <- data.frame()
  for (i in names(subj_responses)) {
    res <- bind_rows(res, 
                     1 - abs(subj_responses - as.numeric(subj_responses[i])))
  }
  
  if (long == T) {
    res <- res %>%
      as.matrix() %>%
      get_upper_tri_fun() %>%
      data.frame() %>%
      mutate(mc1 = names(subj_responses)) %>%
      gather(mc2, score, -mc1) %>%
      mutate_at(vars(mc1, mc2), ~ gsub(" ", ".", .)) %>%
      mutate_at(vars(mc1, mc2), ~ gsub("sick.*$", "sick", .)) %>%
      filter(!is.na(score), mc1 != mc2) %>%
      unite(pair, c(mc1, mc2))
  }
  return(res)
}
```

```{r}
# function for compiling all individual models in a sample
mod_indiv_multi_fun <- function(df_w, kinda_val = 0.5, long = T) {
  
  indiv_mods <- data.frame(pair = character())
  
  for (i in 1:nrow(df_w)) {
    mod <- mod_indiv_fun(df_w, subj = i, kinda = kinda_val, long = long) %>%
      rename_at(vars(score), ~ rownames(df_w)[i])
    indiv_mods <- full_join(indiv_mods, mod)
  }
  
  indiv_mods <- indiv_mods %>%
    arrange(pair)
  
  return(indiv_mods)
}
```

```{r}
# function for getting cultural ("group") model from individuals
mod_cult_fun <- function(df_w, kinda = 0.5, long_val = T, rescale = F) {
  
  res <- mod_indiv_multi_fun(df_w, kinda_val = kinda, long = long_val) %>%
    gather(subj_id, match, -pair) %>%
    group_by(pair) %>%
    summarise(mean_match = mean(match, na.rm = T)) %>%
    ungroup() %>%
    data.frame() %>%
    arrange(pair)

  if (rescale == T) {
    res <- res %>%
      mutate_if(is.numeric, ~scales::rescale(., to = c(0, 1))) %>%
      data.frame()
  }
  
  return(res)
}
```

```{r}
# function for calculating the theoretical min and max MSE based on cultural model
mse_minmax_fun <- function(mod_cult, kinda = 0.5) {
  
  if (kinda == 0.5) {
    d <- mod_cult %>%
      mutate(best_ans = case_when(mean_match > 2/3 ~ 1,
                                  mean_match < 1/3 ~ 0,
                                  mean_match > 1/3 & mean_match < 2/3 ~ 0.5,
                                  TRUE ~ NA_real_),
             worst_ans = case_when(mean_match >= 0.5 ~ 0,
                                   mean_match < 0.5 ~ 1,
                                   TRUE ~ NA_real_))
  } 
  
  if (kinda == 1) {
    d <- mod_cult %>%
      mutate(best_ans = case_when(mean_match >= 0.5 ~ 1,
                                  mean_match < 0.5 ~ 0,
                                  TRUE ~ NA_real_),
             worst_ans = case_when(mean_match >= 0.5 ~ 0,
                                   mean_match < 0.5 ~ 1,
                                   TRUE ~ NA_real_))
  }
  
  d <- d %>%
    mutate(best_diff = best_ans - mean_match,
           worst_diff = worst_ans - mean_match,
           best_sq_diff = best_diff^2,
           worst_sq_diff = worst_diff^2) %>%
    summarise(best_mse = mean(best_sq_diff, na.rm = T),
              worst_mse = mean(worst_sq_diff, na.rm = T))
  
  return(d)
    
}
```

```{r}
# function for comparing all individuals to the cultural ("group") model using MSE
comp_mod_fun <- function(df_w_cult, df_w_indiv = NULL, 
                         kinda_val = 0.5, long_val = T, rescale_val = F) {

  if (is.null(df_w_indiv)) {
    df_w_indiv = df_w_cult
  }
  
  indiv_mods <- mod_indiv_multi_fun(df_w_indiv, kinda = kinda_val, long = long_val) %>%
    arrange(pair)
  
  cult_mod <- mod_cult_fun(df_w_cult, kinda = kinda_val, 
                           long = long_val, rescale = rescale_val) %>%
    arrange(pair)
  
  
  similar <- data.frame(subj_id = character(), MSE = numeric())
  
  for (i in names(indiv_mods)[2:ncol(indiv_mods)]) {
    mse <- mean((indiv_mods[,i] - cult_mod[,"mean_match"])^2, na.rm = T)
    similar <- bind_rows(similar,
                         data.frame(subj_id = i, MSE = mse))
  }
  
  d_minmax <- mse_minmax_fun(cult_mod)
  
  similar <- similar %>%
    mutate(MSE_rescaled = scales::rescale(MSE, to = c(0, 1), 
                                          from = c(d_minmax$best_mse, d_minmax$worst_mse)))
  
  return(similar)
  
}
```



# US

## US adults

```{r}
d_sim_us_adults <- comp_mod_fun(d_us_adults_w) %>% 
  left_join(d_us_adults %>% distinct(country, subj_id, target)) %>%
  left_join(plog_us_adults) %>%
  mutate(target = factor(target, levels = levels_target_univ),
         target = droplevels(target))
```

```{r}
contrasts(d_sim_us_adults$gender) <- cbind("_m" = c(-1, 1))
contrasts(d_sim_us_adults$target) <- contr.sum(length(levels(factor(d_sim_us_adults$target))))
contrasts(d_sim_us_adults$urban_rural_cat2) <- cbind("_rural" = c(-1, 1))
contrasts(d_sim_us_adults$education_cat2) <- cbind("_coll" = c(-1, 1))
contrasts(d_sim_us_adults$ethnicity_cat2) <- cbind("_POC" = c(-1, 1))
contrasts(d_sim_us_adults$religion_cat3) <- cbind("_christian" = c(-1, 1, 0),
                                                  "_other" = c(-1, 0, 1))
```

### Age

```{r}
d_sim_us_adults %>%
  ggplot(aes(x = age, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_smooth(method = "loess", span = 1)
```

```{r}
lm(scale(MSE) ~ scale(age), data = d_sim_us_adults) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age), data = d_sim_us_adults) %>% summary()
```


### Gender

```{r}
d_sim_us_adults %>%
  ggplot(aes(x = gender, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(gender) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(gender) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ gender, data = d_sim_us_adults) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ gender, data = d_sim_us_adults) %>% summary()
```

### Race/ethnicity

```{r}
d_sim_us_adults %>%
  ggplot(aes(x = ethnicity_cat, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(ethnicity_cat) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(ethnicity_cat) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))
```

```{r}
d_sim_us_adults %>%
  ggplot(aes(x = ethnicity_cat2, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(ethnicity_cat2) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(ethnicity_cat2) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ ethnicity_cat2, data = d_sim_us_adults) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ ethnicity_cat2, data = d_sim_us_adults) %>% summary()
```

### Education

```{r}
d_sim_us_adults %>%
  ggplot(aes(x = education_catX, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(education_catX) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(education_catX) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))
```

```{r}
lm(scale(MSE) ~ scale(education_catX), 
   data = d_sim_us_adults %>% mutate(education_catX = as.numeric(education_catX))) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ scale(education_catX), 
        data = d_sim_us_adults %>% mutate(education_catX = as.numeric(education_catX))) %>% summary()
```

```{r}
d_sim_us_adults %>%
  ggplot(aes(x = education_cat2, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(education_cat2) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(education_cat2) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))
```

```{r}
lm(scale(MSE) ~ education_cat2, data = d_sim_us_adults) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ education_cat2, data = d_sim_us_adults) %>% summary()
```

### Rural/urban

```{r}
d_sim_us_adults %>%
  ggplot(aes(x = urban_rural_cat2, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(urban_rural_cat2) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(urban_rural_cat2) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ urban_rural_cat2, data = d_sim_us_adults) %>% summary()
```


```{r}
betareg(MSE_rescaled ~ urban_rural_cat2, data = d_sim_us_adults) %>% summary()
```

### Religion

```{r}
d_sim_us_adults %>%
  ggplot(aes(x = religion_cat3, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(religion_cat3) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(religion_cat3) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ religion_cat3, data = d_sim_us_adults) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ religion_cat3, data = d_sim_us_adults) %>% summary()
```

### Target

```{r}
d_sim_us_adults %>%
  mutate(target = factor(target, levels = levels_target_univ)) %>%
  ggplot(aes(x = target, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(target) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(target) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean), 
                  position = position_dodge(width = 0.25))
```

```{r}
lm(scale(MSE) ~ target, data = d_sim_us_adults, contrasts = list(target = "contr.sum")) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ target, data = d_sim_us_adults) %>% summary()
```

### All together

```{r}
lm(scale(MSE) ~ scale(age) + gender + 
     scale(education_catX) + ethnicity_cat2 + religion_cat3 + urban_rural_cat2 + 
     target, 
   data = d_sim_us_adults %>%
     mutate(education_catX = as.numeric(education_catX))) %>% 
  summary()
```

```{r}
lm(scale(MSE) ~ scale(age) + gender + 
     education_cat2 + ethnicity_cat2 + religion_cat3 + urban_rural_cat2 + 
     target, 
   data = d_sim_us_adults) %>% 
  summary()
```

```{r}
lmer(scale(MSE) ~ scale(age) + gender + 
       scale(education_catX) + ethnicity_cat2 + religion_cat3 + urban_rural_cat2 + 
       + (1 | target), 
     data = d_sim_us_adults %>%
       mutate(education_catX = as.numeric(education_catX))) %>% 
  summary()
```

```{r}
lmer(scale(MSE) ~ scale(age) + gender + 
       education_cat2 + ethnicity_cat2 + religion_cat3 + urban_rural_cat2 + 
       + (1 | target), 
     data = d_sim_us_adults) %>% 
  summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age) + gender + 
          scale(education_catX) + ethnicity_cat2 + religion_cat3 + urban_rural_cat2 + 
          target, 
        data = d_sim_us_adults %>%
          mutate(education_catX = as.numeric(education_catX))) %>% 
  summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age) + gender + 
     education_cat2 + ethnicity_cat2 + religion_cat3 + urban_rural_cat2 + 
     target, 
   data = d_sim_us_adults %>%
     mutate(education_catX = as.numeric(education_catX))) %>% 
  summary()
```

## US children

```{r}
d_sim_us_children <- comp_mod_fun(d_us_children_w) %>% 
  left_join(d_us_children %>% distinct(country, subj_id, target)) %>%
  left_join(plog_us_children) %>%
  mutate(target = factor(target, levels = levels_target_univ),
         target = droplevels(target))
```

```{r}
contrasts(d_sim_us_children$gender) <- cbind("_m" = c(-1, 1))
contrasts(d_sim_us_children$target) <- contr.sum(length(levels(factor(d_sim_us_children$target))))
contrasts(d_sim_us_children$ethnicity_cat2) <- cbind("_POC" = c(-1, 1))
contrasts(d_sim_us_children$religion_cat3) <- cbind("_christian" = c(-1, 1, 0),
                                                  "_other" = c(-1, 0, 1))
```

### Age

```{r}
d_sim_us_children %>%
  ggplot(aes(x = age, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_smooth(method = "loess", span = 1)
```
```{r}
lm(scale(MSE) ~ scale(age), data = d_sim_us_children) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age), data = d_sim_us_children) %>% summary()
```


### Gender

```{r}
d_sim_us_children %>%
  ggplot(aes(x = gender, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(gender) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(gender) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ gender, data = d_sim_us_children) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ gender, data = d_sim_us_children) %>% summary()
```

### Race/ethnicity

```{r}
d_sim_us_children %>%
  ggplot(aes(x = ethnicity_cat, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(ethnicity_cat) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(ethnicity_cat) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
d_sim_us_children %>%
  ggplot(aes(x = ethnicity_cat2, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(ethnicity_cat2) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(ethnicity_cat2) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ ethnicity_cat2, data = d_sim_us_children) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ ethnicity_cat2, data = d_sim_us_children) %>% summary()
```

### Religion

```{r}
d_sim_us_children %>%
  ggplot(aes(x = religion_cat3, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(religion_cat3) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(religion_cat3) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ religion_cat3, data = d_sim_us_children) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ religion_cat3, data = d_sim_us_children) %>% summary()
```

### Target

```{r}
d_sim_us_children %>%
  mutate(target = factor(target, levels = levels_target_univ)) %>%
  ggplot(aes(x = target, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(target) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(target) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean), 
                  position = position_dodge(width = 0.25))
```

```{r}
lm(scale(MSE) ~ target, data = d_sim_us_children, contrasts = list(target = "contr.sum")) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ target, data = d_sim_us_children) %>% summary()
```

### All together

```{r}
lm(scale(MSE) ~ scale(age) + gender + 
     ethnicity_cat2 + religion_cat3 + 
     target, 
   data = d_sim_us_children) %>% 
  summary()
```


```{r}
lmer(scale(MSE) ~ scale(age) + gender + 
       ethnicity_cat2 + religion_cat3 + 
       + (1 | target), 
     data = d_sim_us_children) %>% 
  summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age) + gender + 
          ethnicity_cat2 + religion_cat3 + 
          target, 
        data = d_sim_us_children) %>% 
  summary()
```

# Ghana

## Ghana adults

```{r}
d_sim_gh_adults <- comp_mod_fun(d_gh_adults_w) %>% 
  left_join(d_gh_adults %>% distinct(country, subj_id, target)) %>%
  left_join(plog_gh_adults) %>%
  mutate(target = factor(target, levels = levels_target_univ),
         target = droplevels(target))
```

```{r}
contrasts(d_sim_gh_adults$gender) <- cbind("_m" = c(-1, 1))
contrasts(d_sim_gh_adults$target) <- contr.sum(length(levels(factor(d_sim_gh_adults$target))))
contrasts(d_sim_gh_adults$urban_rural_cat2) <- cbind("_rural" = c(-1, 1))
contrasts(d_sim_gh_adults$education_cat2) <- cbind("_hs" = c(-1, 1))
contrasts(d_sim_gh_adults$ethnicity_cat2) <- cbind("_nonFante" = c(-1, 1))
contrasts(d_sim_gh_adults$religion_cat3) <- cbind("_christian" = c(-1, 1, 0),
                                                  "_other" = c(-1, 0, 1))
```

### Age

```{r}
d_sim_gh_adults %>%
  ggplot(aes(x = age, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_smooth(method = "loess", span = 1)
```
```{r}
lm(scale(MSE) ~ scale(age), data = d_sim_gh_adults) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age), data = d_sim_gh_adults) %>% summary()
```


### Gender

```{r}
d_sim_gh_adults %>%
  ggplot(aes(x = gender, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(gender) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(gender) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ gender, data = d_sim_gh_adults) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ gender, data = d_sim_gh_adults) %>% summary()
```

### Race/ethnicity

```{r}
d_sim_gh_adults %>%
  ggplot(aes(x = ethnicity_cat, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(ethnicity_cat) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(ethnicity_cat) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))
```

```{r}
d_sim_gh_adults %>%
  ggplot(aes(x = ethnicity_cat2, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(ethnicity_cat2) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(ethnicity_cat2) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ ethnicity_cat2, data = d_sim_gh_adults) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ ethnicity_cat2, data = d_sim_gh_adults) %>% summary()
```

### Education

```{r}
d_sim_gh_adults %>%
  ggplot(aes(x = education_catX, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(education_catX) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(education_catX) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))
```

```{r}
lm(scale(MSE) ~ scale(education_catX), 
   data = d_sim_gh_adults %>% mutate(education_catX = as.numeric(education_catX))) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ scale(education_catX), 
        data = d_sim_gh_adults %>% mutate(education_catX = as.numeric(education_catX))) %>% summary()
```

```{r}
d_sim_gh_adults %>%
  ggplot(aes(x = education_cat2, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(education_cat2) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(education_cat2) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ education_cat2, data = d_sim_gh_adults) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ education_cat2, data = d_sim_gh_adults) %>% summary()
```

### Rural/urban

```{r}
d_sim_gh_adults %>%
  ggplot(aes(x = urban_rural_cat2, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(urban_rural_cat2) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(urban_rural_cat2) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ urban_rural_cat2, data = d_sim_gh_adults) %>% summary()
```


```{r}
betareg(MSE_rescaled ~ urban_rural_cat2, data = d_sim_gh_adults) %>% summary()
```

### Religion

```{r}
d_sim_gh_adults %>%
  ggplot(aes(x = religion_cat3, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(religion_cat3) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(religion_cat3) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
# lm(scale(MSE) ~ religion_cat3, data = d_sim_gh_adults) %>% summary() # almost all christian
```

```{r}
# betareg(MSE_rescaled ~ religion_cat3, data = d_sim_gh_adults) %>% summary() # almost all christian
```

### Target

```{r}
d_sim_gh_adults %>%
  mutate(target = factor(target, levels = levels_target_univ)) %>%
  ggplot(aes(x = target, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(target) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(target) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean), 
                  position = position_dodge(width = 0.25))
```

```{r}
lm(scale(MSE) ~ target, data = d_sim_gh_adults, contrasts = list(target = "contr.sum")) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ target, data = d_sim_gh_adults) %>% summary()
```

### All together

```{r}
lm(scale(MSE) ~ scale(age) + gender + 
     scale(education_catX) + ethnicity_cat2 + 
     # religion_cat3 + # almost all christian 
     urban_rural_cat2 + 
     target, 
   data = d_sim_gh_adults %>%
     mutate(education_catX = as.numeric(education_catX))) %>% 
  summary()
```

```{r}
lm(scale(MSE) ~ scale(age) + gender + 
     education_cat2 + ethnicity_cat2 + 
     # religion_cat3 + # almost all christian 
     urban_rural_cat2 + 
     target, 
   data = d_sim_gh_adults) %>% 
  summary()
```

```{r}
lmer(scale(MSE) ~ scale(age) + gender + 
       scale(education_catX) + ethnicity_cat2 + 
       # religion_cat3 + # almost all christian 
       urban_rural_cat2 + 
       + (1 | target), 
     data = d_sim_gh_adults %>%
       mutate(education_catX = as.numeric(education_catX))) %>% 
  summary()
```

```{r}
lmer(scale(MSE) ~ scale(age) + gender + 
       education_cat2 + ethnicity_cat2 + 
       # religion_cat3 + # almost all christian 
       urban_rural_cat2 + 
       + (1 | target), 
     data = d_sim_gh_adults) %>% 
  summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age) + gender + 
          scale(education_catX) + ethnicity_cat2 + 
          # religion_cat3 + # almost all christian 
          urban_rural_cat2 + 
          target, 
        data = d_sim_gh_adults %>%
          mutate(education_catX = as.numeric(education_catX))) %>% 
  summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age) + gender + 
          education_cat2 + ethnicity_cat2 + 
          # religion_cat3 + # almost all christian 
          urban_rural_cat2 + 
          target, 
        data = d_sim_gh_adults %>%
          mutate(education_catX = as.numeric(education_catX))) %>% 
  summary()
```

## Ghana children

```{r}
d_sim_gh_children <- comp_mod_fun(d_gh_children_w) %>% 
  left_join(d_gh_children %>% distinct(country, subj_id, target)) %>%
  left_join(plog_gh_children) %>%
  mutate(target = factor(target, levels = levels_target_univ),
         target = droplevels(target))
```

```{r}
contrasts(d_sim_gh_children$gender) <- cbind("_m" = c(-1, 1))
contrasts(d_sim_gh_children$target) <- contr.sum(length(levels(factor(d_sim_gh_children$target))))
contrasts(d_sim_gh_children$religion_cat3) <- cbind("_christian" = c(-1, 1, 0),
                                                  "_other" = c(-1, 0, 1))
```

### Age

```{r}
d_sim_gh_children %>%
  ggplot(aes(x = age, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_smooth(method = "loess", span = 1)
```
```{r}
lm(scale(MSE) ~ scale(age), data = d_sim_gh_children) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age), data = d_sim_gh_children) %>% summary()
```


### Gender

```{r}
d_sim_gh_children %>%
  ggplot(aes(x = gender, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(gender) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(gender) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ gender, data = d_sim_gh_children) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ gender, data = d_sim_gh_children) %>% summary()
```

### Religion

```{r}
d_sim_gh_children %>%
  ggplot(aes(x = religion_cat3, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(religion_cat3) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(religion_cat3) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
# lm(scale(MSE) ~ religion_cat3, data = d_sim_gh_children) %>% summary() # almost all christian
```

```{r}
# betareg(MSE_rescaled ~ religion_cat3, data = d_sim_gh_children) %>% summary() # almost all christian
```

### Target

```{r}
d_sim_gh_children %>%
  mutate(target = factor(target, levels = levels_target_univ)) %>%
  ggplot(aes(x = target, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(target) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(target) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean), 
                  position = position_dodge(width = 0.25))
```

```{r}
lm(scale(MSE) ~ target, data = d_sim_gh_children, contrasts = list(target = "contr.sum")) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ target, data = d_sim_gh_children) %>% summary()
```

### All together

```{r}
lm(scale(MSE) ~ scale(age) + gender + 
     # religion_cat3 + # almost all christian
     target, 
   data = d_sim_gh_children) %>% 
  summary()
```


```{r}
lmer(scale(MSE) ~ scale(age) + gender + 
       # religion_cat3 + # almost all christian
       + (1 | target), 
     data = d_sim_gh_children) %>% 
  summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age) + gender + 
          # religion_cat3 + almost all christian 
          target, 
        data = d_sim_gh_children) %>% 
  summary()
```


# Thailand

## Thailand adults

```{r}
d_sim_th_adults <- comp_mod_fun(d_th_adults_w) %>% 
  left_join(d_th_adults %>% distinct(country, subj_id, target)) %>%
  left_join(plog_th_adults) %>%
  mutate(target = factor(target, levels = levels_target_univ),
         target = droplevels(target))
```

```{r}
contrasts(d_sim_th_adults$gender) <- cbind("_m" = c(-1, 1, 0),
                                           "_o" = c(-1, 0, 1))
contrasts(d_sim_th_adults$target) <- contr.sum(length(levels(factor(d_sim_th_adults$target))))
contrasts(d_sim_th_adults$urban_rural_cat2) <- cbind("_rural" = c(-1, 1))
contrasts(d_sim_th_adults$education_cat2) <- cbind("_coll" = c(-1, 1))
contrasts(d_sim_th_adults$ethnicity_cat2) <- cbind("_nonThai" = c(-1, 1))
contrasts(d_sim_th_adults$religion_cat3) <- cbind("_christian" = c(-1, 1, 0),
                                                  "_other" = c(-1, 0, 1))
```

### Age

```{r}
d_sim_th_adults %>%
  ggplot(aes(x = age, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_smooth(method = "loess", span = 1)
```

```{r}
lm(scale(MSE) ~ scale(age), data = d_sim_th_adults) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age), data = d_sim_th_adults) %>% summary()
```


### Gender

```{r}
d_sim_th_adults %>%
  ggplot(aes(x = gender, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(gender) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(gender) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ gender, data = d_sim_th_adults) %>% summary()
```

```{r}
lm(scale(MSE) ~ gender, 
   data = d_sim_th_adults %>% filter(gender != "other"),
   contrasts = list(gender = "contr.sum")) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ gender, 
        data = d_sim_th_adults %>%
          filter(gender != "other") %>%
          # effect-code
          mutate(gender = case_when(gender == "female" ~ -1,
                                    gender == "male" ~ 1))) %>% summary()
```


### Race/ethnicity

```{r}
d_sim_th_adults %>%
  ggplot(aes(x = ethnicity_cat, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(ethnicity_cat) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(ethnicity_cat) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
# lm(scale(MSE) ~ ethnicity_cat2, data = d_sim_th_adults) %>% summary() # all thai
```

```{r}
# betareg(MSE_rescaled ~ ethnicity_cat2, data = d_sim_th_adults) %>% summary() # all thai
```

### Education

```{r}
d_sim_th_adults %>%
  ggplot(aes(x = education_catX, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(education_catX) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(education_catX) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))
```

```{r}
lm(scale(MSE) ~ scale(education_catX), 
   data = d_sim_th_adults %>% mutate(education_catX = as.numeric(education_catX))) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ scale(education_catX), 
        data = d_sim_th_adults %>% mutate(education_catX = as.numeric(education_catX))) %>% summary()
```

```{r}
d_sim_th_adults %>%
  ggplot(aes(x = education_cat2, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(education_cat2) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(education_cat2) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ education_cat2, data = d_sim_th_adults) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ education_cat2, data = d_sim_th_adults) %>% summary()
```

### Rural/urban

```{r}
d_sim_th_adults %>%
  ggplot(aes(x = urban_rural_cat2, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(urban_rural_cat2) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(urban_rural_cat2) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ urban_rural_cat2, data = d_sim_th_adults) %>% summary()
```


```{r}
betareg(MSE_rescaled ~ urban_rural_cat2, data = d_sim_th_adults) %>% summary()
```

### Religion

```{r}
d_sim_th_adults %>%
  ggplot(aes(x = religion_cat3, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(religion_cat3) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(religion_cat3) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
# lm(scale(MSE) ~ religion_cat3, data = d_sim_th_adults) %>% summary() # almost all buddhist
```

```{r}
# betareg(MSE_rescaled ~ religion_cat3, data = d_sim_th_adults) %>% summary() # almost all buddhist
```

### Target

```{r}
d_sim_th_adults %>%
  mutate(target = factor(target, levels = levels_target_univ)) %>%
  ggplot(aes(x = target, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(target) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(target) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean), 
                  position = position_dodge(width = 0.25))
```

```{r}
lm(scale(MSE) ~ target, data = d_sim_th_adults, contrasts = list(target = "contr.sum")) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ target, data = d_sim_th_adults) %>% summary()
```

### All together

```{r}
lm(scale(MSE) ~ scale(age) + gender + 
     scale(education_catX) + 
     # ethnicity_cat2 + religion_cat3 + almost all thai and buddhist 
     urban_rural_cat2 + 
     target, 
   data = d_sim_th_adults %>%
     filter(gender != "other") %>%
     mutate(education_catX = as.numeric(education_catX),
            gender = case_when(gender == "female" ~ -1,
                               gender == "male" ~ 1))) %>% 
  summary()
```

```{r}
lm(scale(MSE) ~ scale(age) + gender + 
     education_cat2 + 
     # ethnicity_cat2 + religion_cat3 + almost all thai and buddhist 
     urban_rural_cat2 + 
     target, 
   data = d_sim_th_adults %>%
     filter(gender != "other") %>%
     mutate(education_catX = as.numeric(education_catX),
            gender = case_when(gender == "female" ~ -1,
                               gender == "male" ~ 1))) %>% 
  summary()
```

```{r}
lmer(scale(MSE) ~ scale(age) + gender + 
       scale(education_catX) + 
     # ethnicity_cat2 + religion_cat3 + almost all thai and buddhist 
       urban_rural_cat2 + 
       + (1 | target), 
   data = d_sim_th_adults %>%
     filter(gender != "other") %>%
     mutate(education_catX = as.numeric(education_catX),
            gender = case_when(gender == "female" ~ -1,
                               gender == "male" ~ 1))) %>% 
  summary()
```

```{r}
lmer(scale(MSE) ~ scale(age) + gender + 
       education_cat2 + 
     # ethnicity_cat2 + religion_cat3 + almost all thai and buddhist 
       urban_rural_cat2 + 
       + (1 | target), 
   data = d_sim_th_adults %>%
     filter(gender != "other") %>%
     mutate(education_catX = as.numeric(education_catX),
            gender = case_when(gender == "female" ~ -1,
                               gender == "male" ~ 1))) %>% 
  summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age) + gender + 
          scale(education_catX) + 
     # ethnicity_cat2 + religion_cat3 + almost all thai and buddhist 
          urban_rural_cat2 + 
          target, 
   data = d_sim_th_adults %>%
     filter(gender != "other") %>%
     mutate(education_catX = as.numeric(education_catX),
            gender = case_when(gender == "female" ~ -1,
                               gender == "male" ~ 1))) %>% 
  summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age) + gender + 
     education_cat2 + 
       # ethnicity_cat2 + religion_cat3 + almost all thai and buddhist 
       urban_rural_cat2 + 
     target, 
   data = d_sim_th_adults %>%
     filter(gender != "other") %>%
     mutate(education_catX = as.numeric(education_catX),
            gender = case_when(gender == "female" ~ -1,
                               gender == "male" ~ 1))) %>% 
  summary()
```

## Thailand children

```{r}
d_sim_th_children <- comp_mod_fun(d_th_children_w) %>% 
  left_join(d_th_children %>% distinct(country, subj_id, target)) %>%
  left_join(plog_th_children) %>%
  mutate(target = factor(target, levels = levels_target_univ),
         target = droplevels(target))
```

```{r}
contrasts(d_sim_th_children$gender) <- cbind("_m" = c(-1, 1))
contrasts(d_sim_th_children$target) <- contr.sum(length(levels(factor(d_sim_th_children$target))))
contrasts(d_sim_th_children$religion_cat3) <- cbind("_christian" = c(-1, 1, 0),
                                                  "_other" = c(-1, 0, 1))
```

### Age

```{r}
d_sim_th_children %>%
  ggplot(aes(x = age, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_smooth(method = "loess", span = 1)
```
```{r}
lm(scale(MSE) ~ scale(age), data = d_sim_th_children) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age), data = d_sim_th_children) %>% summary()
```


### Gender

```{r}
d_sim_th_children %>%
  ggplot(aes(x = gender, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(gender) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(gender) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ gender, data = d_sim_th_children) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ gender, data = d_sim_th_children) %>% summary()
```

### Religion

```{r}
d_sim_th_children %>%
  ggplot(aes(x = religion_cat3, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(religion_cat3) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(religion_cat3) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
# lm(scale(MSE) ~ religion_cat3, data = d_sim_th_children) %>% summary() # almost all buddhist
```

```{r}
# betareg(MSE_rescaled ~ religion_cat3, data = d_sim_th_children) %>% summary() # almost all buddhist
```

### Target

```{r}
d_sim_th_children %>%
  mutate(target = factor(target, levels = levels_target_univ)) %>%
  ggplot(aes(x = target, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(target) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(target) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean), 
                  position = position_dodge(width = 0.25))
```

```{r}
lm(scale(MSE) ~ target, data = d_sim_th_children, contrasts = list(target = "contr.sum")) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ target, data = d_sim_th_children) %>% summary()
```

### All together

```{r}
lm(scale(MSE) ~ scale(age) + gender + 
     # religion_cat3 + # almost all buddhist 
     target, 
   data = d_sim_th_children) %>% 
  summary()
```


```{r}
lmer(scale(MSE) ~ scale(age) + gender + 
       # religion_cat3 + # almost all buddhist 
       + (1 | target), 
     data = d_sim_th_children) %>% 
  summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age) + gender + 
          # religion_cat3 + # almost all buddhist 
          target, 
        data = d_sim_th_children) %>% 
  summary()
```


# China

## China adults

```{r}
d_sim_ch_adults <- comp_mod_fun(d_ch_adults_w) %>% 
  left_join(d_ch_adults %>% distinct(country, subj_id, target)) %>%
  left_join(plog_ch_adults) %>%
  mutate(target = factor(target, levels = levels_target_univ),
         target = droplevels(target))
```

```{r}
contrasts(d_sim_ch_adults$gender) <- cbind("_m" = c(-1, 1))
contrasts(d_sim_ch_adults$target) <- contr.sum(length(levels(factor(d_sim_ch_adults$target))))
contrasts(d_sim_ch_adults$urban_rural_cat2) <- cbind("_rural" = c(-1, 1))
contrasts(d_sim_ch_adults$education_cat2) <- cbind("_coll" = c(-1, 1))
contrasts(d_sim_ch_adults$ethnicity_cat2) <- cbind("_nonHan" = c(-1, 1))
contrasts(d_sim_ch_adults$religion_cat3) <- cbind("_buddhist" = c(-1, 1, 0),
                                                  "_other" = c(-1, 0, 1))
```

### Age

```{r}
d_sim_ch_adults %>%
  ggplot(aes(x = age, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_smooth(method = "loess", span = 1)
```

```{r}
lm(scale(MSE) ~ scale(age), data = d_sim_ch_adults) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age), data = d_sim_ch_adults) %>% summary()
```


### Gender

```{r}
d_sim_ch_adults %>%
  ggplot(aes(x = gender, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(gender) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(gender) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ gender, data = d_sim_ch_adults) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ gender, data = d_sim_ch_adults) %>% summary()
```

### Race/ethnicity

```{r}
d_sim_ch_adults %>%
  ggplot(aes(x = ethnicity_cat, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(ethnicity_cat) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(ethnicity_cat) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
d_sim_ch_adults %>%
  ggplot(aes(x = ethnicity_cat2, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(ethnicity_cat2) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(ethnicity_cat2) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
# lm(scale(MSE) ~ ethnicity_cat2, data = d_sim_ch_adults) %>% summary() # almost all han
```

```{r}
# betareg(MSE_rescaled ~ ethnicity_cat2, data = d_sim_ch_adults) %>% summary() # almost all han
```

### Education

```{r}
d_sim_ch_adults %>%
  ggplot(aes(x = education_catX, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(education_catX) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(education_catX) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))
```

```{r}
lm(scale(MSE) ~ scale(education_catX), 
   data = d_sim_ch_adults %>% mutate(education_catX = as.numeric(education_catX))) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ scale(education_catX), 
        data = d_sim_ch_adults %>% mutate(education_catX = as.numeric(education_catX))) %>% summary()
```

```{r}
d_sim_ch_adults %>%
  ggplot(aes(x = education_cat2, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(education_cat2) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(education_cat2) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ education_cat2, data = d_sim_ch_adults) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ education_cat2, data = d_sim_ch_adults) %>% summary()
```

### Rural/urban

```{r}
d_sim_ch_adults %>%
  ggplot(aes(x = urban_rural_cat2, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(urban_rural_cat2) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(urban_rural_cat2) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ urban_rural_cat2, data = d_sim_ch_adults) %>% summary()
```


```{r}
betareg(MSE_rescaled ~ urban_rural_cat2, data = d_sim_ch_adults) %>% summary()
```

### Religion

```{r}
d_sim_ch_adults %>%
  ggplot(aes(x = religion_cat3, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(religion_cat3) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(religion_cat3) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ religion_cat3, data = d_sim_ch_adults) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ religion_cat3, data = d_sim_ch_adults) %>% summary()
```

### Target

```{r}
d_sim_ch_adults %>%
  mutate(target = factor(target, levels = levels_target_univ)) %>%
  ggplot(aes(x = target, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(target) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(target) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean), 
                  position = position_dodge(width = 0.25))
```

```{r}
lm(scale(MSE) ~ target, data = d_sim_ch_adults, contrasts = list(target = "contr.sum")) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ target, data = d_sim_ch_adults) %>% summary()
```

### All together

```{r}
lm(scale(MSE) ~ scale(age) + gender + 
     scale(education_catX) + 
     # ethnicity_cat2 + # almost all han
     religion_cat3 + urban_rural_cat2 + 
     target, 
   data = d_sim_ch_adults %>%
     mutate(education_catX = as.numeric(education_catX))) %>% 
  summary()
```

```{r}
lm(scale(MSE) ~ scale(age) + gender + 
     education_cat2 + 
     # ethnicity_cat2 + # almost all han
     religion_cat3 + urban_rural_cat2 + 
     target, 
   data = d_sim_ch_adults) %>% 
  summary()
```

```{r}
lmer(scale(MSE) ~ scale(age) + gender + 
       scale(education_catX) + 
     # ethnicity_cat2 + # almost all han
     religion_cat3 + urban_rural_cat2 + 
       + (1 | target), 
     data = d_sim_ch_adults %>%
       mutate(education_catX = as.numeric(education_catX))) %>% 
  summary()
```

```{r}
lmer(scale(MSE) ~ scale(age) + gender + 
       education_cat2 + 
     # ethnicity_cat2 + # almost all han
     religion_cat3 + urban_rural_cat2 + 
       + (1 | target), 
     data = d_sim_ch_adults) %>% 
  summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age) + gender + 
          scale(education_catX) + 
     # ethnicity_cat2 + # almost all han
     religion_cat3 + urban_rural_cat2 + 
          target, 
        data = d_sim_ch_adults %>%
          mutate(education_catX = as.numeric(education_catX))) %>% 
  summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age) + gender + 
     education_cat2 + 
     # ethnicity_cat2 + # almost all han
     religion_cat3 + urban_rural_cat2 + 
     target, 
   data = d_sim_ch_adults %>%
     mutate(education_catX = as.numeric(education_catX))) %>% 
  summary()
```

## China children

```{r}
d_sim_ch_children <- comp_mod_fun(d_ch_children_w) %>% 
  left_join(d_ch_children %>% distinct(country, subj_id, target)) %>%
  left_join(plog_ch_children) %>%
  mutate(target = factor(target, levels = levels_target_univ),
         target = droplevels(target))
```

```{r}
contrasts(d_sim_ch_children$gender) <- cbind("_m" = c(-1, 1))
contrasts(d_sim_ch_children$target) <- contr.sum(length(levels(factor(d_sim_ch_children$target))))
```

### Age

```{r}
d_sim_ch_children %>%
  ggplot(aes(x = age, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_smooth(method = "loess", span = 1)
```
```{r}
lm(scale(MSE) ~ scale(age), data = d_sim_ch_children) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age), data = d_sim_ch_children) %>% summary()
```


### Gender

```{r}
d_sim_ch_children %>%
  ggplot(aes(x = gender, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(gender) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(gender) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ gender, data = d_sim_ch_children) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ gender, data = d_sim_ch_children) %>% summary()
```

### Target

```{r}
d_sim_ch_children %>%
  mutate(target = factor(target, levels = levels_target_univ)) %>%
  ggplot(aes(x = target, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(target) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(target) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean), 
                  position = position_dodge(width = 0.25))
```

```{r}
lm(scale(MSE) ~ target, data = d_sim_ch_children, contrasts = list(target = "contr.sum")) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ target, data = d_sim_ch_children) %>% summary()
```

### All together

```{r}
lm(scale(MSE) ~ scale(age) + gender + 
     target, 
   data = d_sim_ch_children) %>% 
  summary()
```


```{r}
lmer(scale(MSE) ~ scale(age) + gender + 
       + (1 | target), 
     data = d_sim_ch_children) %>% 
  summary()
```

```{r}
# error ?
# betareg(MSE_rescaled ~ scale(age) + gender + 
#           target, 
#         data = d_sim_ch_children) %>% 
#   summary()
```


# Vanuatu

## Vanuatu adults

```{r}
d_sim_vt_adults <- comp_mod_fun(d_vt_adults_w) %>% 
  left_join(d_vt_adults %>% distinct(country, subj_id, location, target)) %>%
  left_join(plog_vt_adults) %>%
  mutate(target = factor(target, levels = levels_target_univ),
         target = droplevels(target)) %>%
  mutate(location_cat3 = case_when(grepl("Espigles", location) |
                                     grepl("Malekula", location) ~ "rural",
                                   grepl("Fresh Water", location) |
                                     grepl("Namba", location) |
                                     grepl("Port Vila", location) ~ "urban",
                                   grepl("Teouma", location) ~ "periurban",
                                   TRUE ~ NA_character_),
         location_cat3 = factor(location_cat3, levels = c("rural", "periurban", "urban")),
         location_cat2 = case_when(grepl("urban", location_cat3) ~ "urban/periurban",
                                   grepl("rural", location_cat3) ~ "rural",
                                   TRUE ~ NA_character_),
         location_cat2 = factor(location_cat2, levels = c("rural", "urban/periurban")))
```

```{r}
contrasts(d_sim_vt_adults$gender) <- cbind("_m" = c(-1, 1))
contrasts(d_sim_vt_adults$target) <- contr.sum(length(levels(factor(d_sim_vt_adults$target))))
contrasts(d_sim_vt_adults$location_cat2) <- cbind("_urban" = c(-1, 1))
```

### Age

```{r}
d_sim_vt_adults %>%
  ggplot(aes(x = age, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_smooth(method = "loess", span = 1)
```

```{r}
lm(scale(MSE) ~ scale(age), data = d_sim_vt_adults) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age), data = d_sim_vt_adults) %>% summary()
```


### Gender

```{r}
d_sim_vt_adults %>%
  ggplot(aes(x = gender, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(gender) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(gender) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ gender, data = d_sim_vt_adults) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ gender, data = d_sim_vt_adults) %>% summary()
```

### Location

```{r}
d_sim_vt_adults %>%
  ggplot(aes(x = location_cat2, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(location_cat2) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(location_cat2) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ location_cat2, data = d_sim_vt_adults) %>% summary()
```


```{r}
betareg(MSE_rescaled ~ location_cat2, data = d_sim_vt_adults) %>% summary()
```

### Target

```{r}
d_sim_vt_adults %>%
  mutate(target = factor(target, levels = levels_target_univ)) %>%
  ggplot(aes(x = target, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(target) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(target) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean), 
                  position = position_dodge(width = 0.25))
```

```{r}
lm(scale(MSE) ~ target, data = d_sim_vt_adults, contrasts = list(target = "contr.sum")) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ target, data = d_sim_vt_adults) %>% summary()
```

### All together


```{r}
lm(scale(MSE) ~ scale(age) + gender + 
     location_cat2 + 
     target, 
   data = d_sim_vt_adults) %>% 
  summary()
```

```{r}
lmer(scale(MSE) ~ scale(age) + gender + 
     location_cat2 + 
       + (1 | target), 
     data = d_sim_vt_adults) %>% 
  summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age) + gender + 
     location_cat2 + 
          target, 
        data = d_sim_vt_adults) %>% 
  summary()
```


## Vanuatu children

```{r}
d_sim_vt_children <- comp_mod_fun(d_vt_children_w) %>% 
  left_join(d_vt_children %>% distinct(country, subj_id, location, target)) %>%
  left_join(plog_vt_children) %>%
  mutate(target = factor(target, levels = levels_target_univ),
         target = droplevels(target)) %>%
  mutate(location_cat3 = case_when(grepl("espigles", location) |
                                     grepl("malekula", location) |
                                     location %in% c("khomi", "matanvat", "molin", "moliu", "ulam") ~ "rural",
                                   grepl("childcare", location) ~ "urban",
                                   grepl("teouma", location) |
                                     grepl("eratap", location) ~ "periurban",
                                   TRUE ~ NA_character_),
         location_cat3 = factor(location_cat3, levels = c("rural", "periurban", "urban")),
         location_cat2 = case_when(grepl("urban", location_cat3) ~ "urban/periurban",
                                   grepl("rural", location_cat3) ~ "rural",
                                   TRUE ~ NA_character_),
         location_cat2 = factor(location_cat2, levels = c("rural", "urban/periurban")))
```

```{r}
contrasts(d_sim_vt_children$gender) <- cbind("_m" = c(-1, 1))
contrasts(d_sim_vt_children$target) <- contr.sum(length(levels(factor(d_sim_vt_children$target))))
```

### Age

```{r}
d_sim_vt_children %>%
  ggplot(aes(x = age, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_smooth(method = "loess", span = 1)
```
```{r}
lm(scale(MSE) ~ scale(age), data = d_sim_vt_children) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age), data = d_sim_vt_children) %>% summary()
```


### Gender

```{r}
d_sim_vt_children %>%
  ggplot(aes(x = gender, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(gender) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(gender) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ gender, data = d_sim_vt_children) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ gender, data = d_sim_vt_children) %>% summary()
```

### Target

```{r}
d_sim_vt_children %>%
  mutate(target = factor(target, levels = levels_target_univ)) %>%
  ggplot(aes(x = target, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(target) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(target) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean), 
                  position = position_dodge(width = 0.25))
```

```{r}
lm(scale(MSE) ~ target, data = d_sim_vt_children, contrasts = list(target = "contr.sum")) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ target, data = d_sim_vt_children) %>% summary()
```

### All together

```{r}
lm(scale(MSE) ~ scale(age) + gender + 
     target, 
   data = d_sim_vt_children) %>% 
  summary()
```


```{r}
lmer(scale(MSE) ~ scale(age) + gender + 
       + (1 | target), 
     data = d_sim_vt_children) %>% 
  summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age) + gender + 
          target, 
        data = d_sim_vt_children) %>% 
  summary()
```


# Using adults' models for individual children

## US

```{r}
d_sim_us_adch <- comp_mod_fun(df_w_cult = d_us_adults_w, df_w_indiv = d_us_children_w) %>% 
  left_join(d_us_children %>% distinct(country, subj_id, target)) %>%
  left_join(plog_us_children) %>%
  mutate(target = factor(target, levels = levels_target_univ),
         target = droplevels(target))
```

```{r}
contrasts(d_sim_us_adch$gender) <- cbind("_m" = c(-1, 1))
contrasts(d_sim_us_adch$target) <- contr.sum(length(levels(factor(d_sim_us_adch$target))))
contrasts(d_sim_us_adch$ethnicity_cat2) <- cbind("_POC" = c(-1, 1))
contrasts(d_sim_us_adch$religion_cat3) <- cbind("_christian" = c(-1, 1, 0),
                                                  "_other" = c(-1, 0, 1))
```

### Age

```{r}
d_sim_us_adch %>%
  ggplot(aes(x = age, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_smooth(method = "loess", span = 1)
```
```{r}
lm(scale(MSE) ~ scale(age), data = d_sim_us_adch) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age), data = d_sim_us_adch) %>% summary()
```


### Gender

```{r}
d_sim_us_adch %>%
  ggplot(aes(x = gender, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(gender) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(gender) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ gender, data = d_sim_us_adch) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ gender, data = d_sim_us_adch) %>% summary()
```

### Race/ethnicity

```{r}
d_sim_us_adch %>%
  ggplot(aes(x = ethnicity_cat, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(ethnicity_cat) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(ethnicity_cat) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
d_sim_us_adch %>%
  ggplot(aes(x = ethnicity_cat2, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(ethnicity_cat2) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(ethnicity_cat2) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ ethnicity_cat2, data = d_sim_us_adch) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ ethnicity_cat2, data = d_sim_us_adch) %>% summary()
```

### Religion

```{r}
d_sim_us_adch %>%
  ggplot(aes(x = religion_cat3, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(religion_cat3) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(religion_cat3) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ religion_cat3, data = d_sim_us_adch) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ religion_cat3, data = d_sim_us_adch) %>% summary()
```

### Target

```{r}
d_sim_us_adch %>%
  mutate(target = factor(target, levels = levels_target_univ)) %>%
  ggplot(aes(x = target, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(target) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(target) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean), 
                  position = position_dodge(width = 0.25))
```

```{r}
lm(scale(MSE) ~ target, data = d_sim_us_adch, contrasts = list(target = "contr.sum")) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ target, data = d_sim_us_adch) %>% summary()
```

### All together

```{r}
lm(scale(MSE) ~ scale(age) + gender + 
     ethnicity_cat2 + religion_cat3 + 
     target, 
   data = d_sim_us_adch) %>% 
  summary()
```

```{r}
lmer(scale(MSE) ~ scale(age) + gender + 
       ethnicity_cat2 + religion_cat3 + 
       + (1 | target), 
     data = d_sim_us_adch) %>% 
  summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age) + gender + 
          ethnicity_cat2 + religion_cat3 + 
          target, 
        data = d_sim_us_adch) %>% 
  summary()
```


## Ghana

```{r}
d_sim_gh_adch <- comp_mod_fun(df_w_cult = d_gh_adults_w, df_w_indiv = d_gh_children_w) %>% 
  left_join(d_gh_children %>% distinct(country, subj_id, target)) %>%
  left_join(plog_gh_children) %>%
  mutate(target = factor(target, levels = levels_target_univ),
         target = droplevels(target))
```

```{r}
contrasts(d_sim_gh_adch$gender) <- cbind("_m" = c(-1, 1))
contrasts(d_sim_gh_adch$target) <- contr.sum(length(levels(factor(d_sim_gh_adch$target))))
contrasts(d_sim_gh_adch$religion_cat3) <- cbind("_christian" = c(-1, 1, 0),
                                                  "_other" = c(-1, 0, 1))
```

### Age

```{r}
d_sim_gh_adch %>%
  ggplot(aes(x = age, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_smooth(method = "loess", span = 1)
```
```{r}
lm(scale(MSE) ~ scale(age), data = d_sim_gh_adch) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age), data = d_sim_gh_adch) %>% summary()
```


### Gender

```{r}
d_sim_gh_adch %>%
  ggplot(aes(x = gender, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(gender) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(gender) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ gender, data = d_sim_gh_adch) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ gender, data = d_sim_gh_adch) %>% summary()
```

### Religion

```{r}
d_sim_gh_adch %>%
  ggplot(aes(x = religion_cat3, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(religion_cat3) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(religion_cat3) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
# lm(scale(MSE) ~ religion_cat3, data = d_sim_gh_adch) %>% summary() # almost all christian
```

```{r}
# betareg(MSE_rescaled ~ religion_cat3, data = d_sim_gh_adch) %>% summary() # almost all christian
```

### Target

```{r}
d_sim_gh_adch %>%
  mutate(target = factor(target, levels = levels_target_univ)) %>%
  ggplot(aes(x = target, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(target) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(target) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean), 
                  position = position_dodge(width = 0.25))
```

```{r}
lm(scale(MSE) ~ target, data = d_sim_gh_adch, contrasts = list(target = "contr.sum")) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ target, data = d_sim_gh_adch) %>% summary()
```

### All together

```{r}
lm(scale(MSE) ~ scale(age) + gender + 
     # religion_cat3 + # almost all christian
     target, 
   data = d_sim_gh_adch) %>% 
  summary()
```


```{r}
lmer(scale(MSE) ~ scale(age) + gender + 
       # religion_cat3 + # almost all christian
       + (1 | target), 
     data = d_sim_gh_adch) %>% 
  summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age) + gender + 
          # religion_cat3 + almost all christian 
          target, 
        data = d_sim_gh_adch) %>% 
  summary()
```


## Thailand

```{r}
d_sim_th_adch <- comp_mod_fun(d_th_children_w) %>% 
  left_join(d_th_children %>% distinct(country, subj_id, target)) %>%
  left_join(plog_th_children) %>%
  mutate(target = factor(target, levels = levels_target_univ),
         target = droplevels(target))
```

```{r}
contrasts(d_sim_th_adch$gender) <- cbind("_m" = c(-1, 1))
contrasts(d_sim_th_adch$target) <- contr.sum(length(levels(factor(d_sim_th_adch$target))))
contrasts(d_sim_th_adch$religion_cat3) <- cbind("_christian" = c(-1, 1, 0),
                                                  "_other" = c(-1, 0, 1))
```

### Age

```{r}
d_sim_th_adch %>%
  ggplot(aes(x = age, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_smooth(method = "loess", span = 1)
```
```{r}
lm(scale(MSE) ~ scale(age), data = d_sim_th_adch) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age), data = d_sim_th_adch) %>% summary()
```


### Gender

```{r}
d_sim_th_adch %>%
  ggplot(aes(x = gender, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(gender) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(gender) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ gender, data = d_sim_th_adch) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ gender, data = d_sim_th_adch) %>% summary()
```

### Religion

```{r}
d_sim_th_adch %>%
  ggplot(aes(x = religion_cat3, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(religion_cat3) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(religion_cat3) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
# lm(scale(MSE) ~ religion_cat3, data = d_sim_th_adch) %>% summary() # almost all buddhist
```

```{r}
# betareg(MSE_rescaled ~ religion_cat3, data = d_sim_th_adch) %>% summary() # almost all buddhist
```

### Target

```{r}
d_sim_th_adch %>%
  mutate(target = factor(target, levels = levels_target_univ)) %>%
  ggplot(aes(x = target, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(target) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(target) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean), 
                  position = position_dodge(width = 0.25))
```

```{r}
lm(scale(MSE) ~ target, data = d_sim_th_adch, contrasts = list(target = "contr.sum")) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ target, data = d_sim_th_adch) %>% summary()
```

### All together

```{r}
lm(scale(MSE) ~ scale(age) + gender + 
     # religion_cat3 + # almost all buddhist 
     target, 
   data = d_sim_th_adch) %>% 
  summary()
```


```{r}
lmer(scale(MSE) ~ scale(age) + gender + 
       # religion_cat3 + # almost all buddhist 
       + (1 | target), 
     data = d_sim_th_adch) %>% 
  summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age) + gender + 
          # religion_cat3 + # almost all buddhist 
          target, 
        data = d_sim_th_adch) %>% 
  summary()
```


## China

```{r}
d_sim_ch_adch <- comp_mod_fun(df_w_cult = d_ch_adults_w, df_w_indiv = d_ch_children_w) %>% 
  left_join(d_ch_children %>% distinct(country, subj_id, target)) %>%
  left_join(plog_ch_children) %>%
  mutate(target = factor(target, levels = levels_target_univ),
         target = droplevels(target))
```

```{r}
contrasts(d_sim_ch_adch$gender) <- cbind("_m" = c(-1, 1))
contrasts(d_sim_ch_adch$target) <- contr.sum(length(levels(factor(d_sim_ch_adch$target))))
```

### Age

```{r}
d_sim_ch_adch %>%
  ggplot(aes(x = age, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_smooth(method = "loess", span = 1)
```
```{r}
lm(scale(MSE) ~ scale(age), data = d_sim_ch_adch) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age), data = d_sim_ch_adch) %>% summary()
```


### Gender

```{r}
d_sim_ch_adch %>%
  ggplot(aes(x = gender, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(gender) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(gender) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ gender, data = d_sim_ch_adch) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ gender, data = d_sim_ch_adch) %>% summary()
```

### Target

```{r}
d_sim_ch_adch %>%
  mutate(target = factor(target, levels = levels_target_univ)) %>%
  ggplot(aes(x = target, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(target) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(target) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean), 
                  position = position_dodge(width = 0.25))
```

```{r}
lm(scale(MSE) ~ target, data = d_sim_ch_adch, contrasts = list(target = "contr.sum")) %>% summary()
```

```{r}
# betareg(MSE_rescaled ~ target, data = d_sim_ch_adch) %>% summary()
```

### All together

```{r}
lm(scale(MSE) ~ scale(age) + gender + 
     target, 
   data = d_sim_ch_adch) %>% 
  summary()
```


```{r}
lmer(scale(MSE) ~ scale(age) + gender + 
       + (1 | target), 
     data = d_sim_ch_adch) %>% 
  summary()
```

```{r}
# error ?
# betareg(MSE_rescaled ~ scale(age) + gender + 
#           target, 
#         data = d_sim_ch_adch) %>% 
#   summary()
```


## Vanuatu

```{r}
d_sim_vt_adch <- comp_mod_fun(df_w_cult = d_vt_children_w, df_w_indiv = d_vt_children_w) %>% 
  left_join(d_vt_children %>% distinct(country, subj_id, location, target)) %>%
  left_join(plog_vt_children) %>%
  mutate(target = factor(target, levels = levels_target_univ),
         target = droplevels(target)) %>%
  mutate(location_cat3 = case_when(grepl("espigles", location) |
                                     grepl("malekula", location) |
                                     location %in% c("khomi", "matanvat", "molin", "moliu", "ulam") ~ "rural",
                                   grepl("childcare", location) ~ "urban",
                                   grepl("teouma", location) |
                                     grepl("eratap", location) ~ "periurban",
                                   TRUE ~ NA_character_),
         location_cat3 = factor(location_cat3, levels = c("rural", "periurban", "urban")),
         location_cat2 = case_when(grepl("urban", location_cat3) ~ "urban/periurban",
                                   grepl("rural", location_cat3) ~ "rural",
                                   TRUE ~ NA_character_),
         location_cat2 = factor(location_cat2, levels = c("rural", "urban/periurban")))
```

```{r}
contrasts(d_sim_vt_adch$gender) <- cbind("_m" = c(-1, 1))
contrasts(d_sim_vt_adch$target) <- contr.sum(length(levels(factor(d_sim_vt_adch$target))))
```

### Age

```{r}
d_sim_vt_adch %>%
  ggplot(aes(x = age, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_smooth(method = "loess", span = 1)
```
```{r}
lm(scale(MSE) ~ scale(age), data = d_sim_vt_adch) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age), data = d_sim_vt_adch) %>% summary()
```


### Gender

```{r}
d_sim_vt_adch %>%
  ggplot(aes(x = gender, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(gender) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(gender) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ gender, data = d_sim_vt_adch) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ gender, data = d_sim_vt_adch) %>% summary()
```

### Target

```{r}
d_sim_vt_adch %>%
  mutate(target = factor(target, levels = levels_target_univ)) %>%
  ggplot(aes(x = target, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(target) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(target) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean), 
                  position = position_dodge(width = 0.25))
```

```{r}
lm(scale(MSE) ~ target, data = d_sim_vt_adch, contrasts = list(target = "contr.sum")) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ target, data = d_sim_vt_adch) %>% summary()
```

### All together

```{r}
lm(scale(MSE) ~ scale(age) + gender + 
     target, 
   data = d_sim_vt_adch) %>% 
  summary()
```


```{r}
lmer(scale(MSE) ~ scale(age) + gender + 
       + (1 | target), 
     data = d_sim_vt_adch) %>% 
  summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age) + gender + 
          target, 
        data = d_sim_vt_adch) %>% 
  summary()
```











# Using US model for other adults (kinda = yes = 1)

```{r}
d_sim_us_us <- comp_mod_fun(df_w_cult = d_us_adults_w, 
                            df_w_indiv = d_us_adults_w, kinda_val = 1) %>% 
  left_join(d_us_adults %>% distinct(country, subj_id, target)) %>%
  left_join(plog_us_adults) %>%
  mutate(target = factor(target, levels = levels_target_univ),
         target = droplevels(target))
```

```{r}
d_sim_us_gh <- comp_mod_fun(df_w_cult = d_us_adults_w, 
                            df_w_indiv = d_gh_adults_w, kinda_val = 1) %>% 
  left_join(d_gh_adults %>% distinct(country, subj_id, target)) %>%
  left_join(plog_gh_adults) %>%
  mutate(target = factor(target, levels = levels_target_univ),
         target = droplevels(target))
```

```{r}
d_sim_us_th <- comp_mod_fun(df_w_cult = d_us_adults_w, 
                            df_w_indiv = d_th_adults_w, kinda_val = 1) %>% 
  left_join(d_th_adults %>% distinct(country, subj_id, target)) %>%
  left_join(plog_th_adults) %>%
  mutate(target = factor(target, levels = levels_target_univ),
         target = droplevels(target))
```

```{r}
d_sim_us_ch <- comp_mod_fun(df_w_cult = d_us_adults_w, 
                            df_w_indiv = d_ch_adults_w, kinda_val = 1) %>% 
  left_join(d_ch_adults %>% distinct(country, subj_id, target)) %>%
  left_join(plog_ch_adults) %>%
  mutate(target = factor(target, levels = levels_target_univ),
         target = droplevels(target))
```

```{r}
d_sim_us_vt <- comp_mod_fun(df_w_cult = d_us_adults_w, 
                            df_w_indiv = d_vt_adults_w, kinda_val = 1) %>% 
  left_join(d_vt_adults %>% distinct(country, subj_id, location, target)) %>%
  left_join(plog_vt_adults) %>%
  mutate(target = factor(target, levels = levels_target_univ),
         target = droplevels(target)) %>%
  mutate(location_cat3 = case_when(grepl("Espigles", location) |
                                     grepl("Malekula", location) ~ "rural",
                                   grepl("Fresh Water", location) |
                                     grepl("Namba", location) |
                                     grepl("Port Vila", location) ~ "urban",
                                   grepl("Teouma", location) ~ "periurban",
                                   TRUE ~ NA_character_),
         location_cat3 = factor(location_cat3, levels = c("rural", "periurban", "urban")),
         location_cat2 = case_when(grepl("urban", location_cat3) ~ "urban/periurban",
                                   grepl("rural", location_cat3) ~ "rural",
                                   TRUE ~ NA_character_),
         location_cat2 = factor(location_cat2, levels = c("rural", "urban/periurban")))
```

```{r}
full_join(bind_rows(d_sim_us_us, d_sim_us_gh, d_sim_us_th, 
                    d_sim_us_ch, d_sim_us_vt) %>%
            mutate(cult_mod = "US adults"),
          bind_rows(d_sim_us_adults, d_sim_gh_adults, d_sim_th_adults, 
                    d_sim_ch_adults, d_sim_vt_adults) %>%
            mutate(cult_mod = "Own sample")) %>%
  mutate(country = factor(country, levels = levels_country)) %>%
  ggplot(aes(x = country, y = MSE, color = country, shape = cult_mod)) +
  geom_point(alpha = 0.2, position = position_jitterdodge(jitter.height = 0, dodge.width = 0.5)) +
  geom_pointrange(data = . %>%
                    group_by(country, cult_mod) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(y = mean, ymin = ci_lower, ymax = ci_upper),
                  position = position_dodge(width = 0.5),
                  color = "black") + 
  scale_color_brewer(palette = "Dark2") +
  scale_y_continuous(limits = c(0, NA))
```
```{r}
full_join(bind_rows(d_sim_us_us, d_sim_us_gh, d_sim_us_th, 
                    d_sim_us_ch, d_sim_us_vt) %>%
            mutate(cult_mod = "US adults"),
          bind_rows(d_sim_us_adults, d_sim_gh_adults, d_sim_th_adults, 
                    d_sim_ch_adults, d_sim_vt_adults) %>%
            mutate(cult_mod = "Own sample")) %>%
  mutate(country = factor(country, levels = levels_country)) %>%
  ggplot(aes(x = country, y = MSE_rescaled, color = country, shape = cult_mod)) +
  geom_point(alpha = 0.2, position = position_jitterdodge(jitter.height = 0, dodge.width = 0.5)) +
  geom_pointrange(data = . %>%
                    group_by(country, cult_mod) %>%
                    multi_boot_standard(col = "MSE_rescaled", na.rm = T),
                  aes(y = mean, ymin = ci_lower, ymax = ci_upper),
                  position = position_dodge(width = 0.5),
                  color = "black") + 
  scale_color_brewer(palette = "Dark2") +
  scale_y_continuous(limits = c(0, NA))
```

## Ghana

```{r}
contrasts(d_sim_us_gh$gender) <- cbind("_m" = c(-1, 1))
contrasts(d_sim_us_gh$target) <- contr.sum(length(levels(factor(d_sim_us_gh$target))))
contrasts(d_sim_us_gh$urban_rural_cat2) <- cbind("_rural" = c(-1, 1))
contrasts(d_sim_us_gh$education_cat2) <- cbind("_hs" = c(-1, 1))
contrasts(d_sim_us_gh$ethnicity_cat2) <- cbind("_nonFante" = c(-1, 1))
contrasts(d_sim_us_gh$religion_cat3) <- cbind("_christian" = c(-1, 1, 0),
                                                  "_other" = c(-1, 0, 1))
```

### Age

```{r}
d_sim_us_gh %>%
  ggplot(aes(x = age, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_smooth(method = "loess", span = 1)
```
```{r}
lm(scale(MSE) ~ scale(age), data = d_sim_us_gh) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age), data = d_sim_us_gh) %>% summary()
```


### Gender

```{r}
d_sim_us_gh %>%
  ggplot(aes(x = gender, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(gender) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(gender) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ gender, data = d_sim_us_gh) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ gender, data = d_sim_us_gh) %>% summary()
```

### Race/ethnicity

```{r}
d_sim_us_gh %>%
  ggplot(aes(x = ethnicity_cat, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(ethnicity_cat) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(ethnicity_cat) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
d_sim_us_gh %>%
  ggplot(aes(x = ethnicity_cat2, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(ethnicity_cat2) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(ethnicity_cat2) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ ethnicity_cat2, data = d_sim_us_gh) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ ethnicity_cat2, data = d_sim_us_gh) %>% summary()
```

### Education

```{r}
d_sim_us_gh %>%
  ggplot(aes(x = education_catX, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(education_catX) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(education_catX) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ scale(education_catX), 
   data = d_sim_us_gh %>% mutate(education_catX = as.numeric(education_catX))) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ scale(education_catX), 
        data = d_sim_us_gh %>% mutate(education_catX = as.numeric(education_catX))) %>% summary()
```

```{r}
d_sim_us_gh %>%
  ggplot(aes(x = education_cat2, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(education_cat2) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(education_cat2) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ education_cat2, data = d_sim_us_gh) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ education_cat2, data = d_sim_us_gh) %>% summary()
```

### Rural/urban

```{r}
d_sim_us_gh %>%
  ggplot(aes(x = urban_rural_cat2, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(urban_rural_cat2) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(urban_rural_cat2) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ urban_rural_cat2, data = d_sim_us_gh) %>% summary()
```


```{r}
betareg(MSE_rescaled ~ urban_rural_cat2, data = d_sim_us_gh) %>% summary()
```

### Religion

```{r}
d_sim_us_gh %>%
  ggplot(aes(x = religion_cat3, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(religion_cat3) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(religion_cat3) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
# lm(scale(MSE) ~ religion_cat3, data = d_sim_us_gh) %>% summary() # almost all christian
```

```{r}
# betareg(MSE_rescaled ~ religion_cat3, data = d_sim_us_gh) %>% summary() # almost all christian
```

### Target

```{r}
d_sim_us_gh %>%
  mutate(target = factor(target, levels = levels_target_univ)) %>%
  ggplot(aes(x = target, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(target) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(target) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean), 
                  position = position_dodge(width = 0.25))
```

```{r}
lm(scale(MSE) ~ target, data = d_sim_us_gh, contrasts = list(target = "contr.sum")) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ target, data = d_sim_us_gh) %>% summary()
```

### All together

```{r}
lm(scale(MSE) ~ scale(age) + gender + 
     scale(education_catX) + ethnicity_cat2 + 
     # religion_cat3 + # almost all christian 
     urban_rural_cat2 + 
     target, 
   data = d_sim_us_gh %>%
     mutate(education_catX = as.numeric(education_catX))) %>% 
  summary()
```

```{r}
lm(scale(MSE) ~ scale(age) + gender + 
     education_cat2 + ethnicity_cat2 + 
     # religion_cat3 + # almost all christian 
     urban_rural_cat2 + 
     target, 
   data = d_sim_us_gh) %>% 
  summary()
```

```{r}
lmer(scale(MSE) ~ scale(age) + gender + 
       scale(education_catX) + ethnicity_cat2 + 
       # religion_cat3 + # almost all christian 
       urban_rural_cat2 + 
       + (1 | target), 
     data = d_sim_us_gh %>%
       mutate(education_catX = as.numeric(education_catX))) %>% 
  summary()
```

```{r}
lmer(scale(MSE) ~ scale(age) + gender + 
       education_cat2 + ethnicity_cat2 + 
       # religion_cat3 + # almost all christian 
       urban_rural_cat2 + 
       + (1 | target), 
     data = d_sim_us_gh) %>% 
  summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age) + gender + 
          scale(education_catX) + ethnicity_cat2 + 
          # religion_cat3 + # almost all christian 
          urban_rural_cat2 + 
          target, 
        data = d_sim_us_gh %>%
          mutate(education_catX = as.numeric(education_catX))) %>% 
  summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age) + gender + 
          education_cat2 + ethnicity_cat2 + 
          # religion_cat3 + # almost all christian 
          urban_rural_cat2 + 
          target, 
        data = d_sim_us_gh %>%
          mutate(education_catX = as.numeric(education_catX))) %>% 
  summary()
```

## Thailand

```{r}
contrasts(d_sim_us_th$gender) <- cbind("_m" = c(-1, 1, 0),
                                           "_o" = c(-1, 0, 1))
contrasts(d_sim_us_th$target) <- contr.sum(length(levels(factor(d_sim_us_th$target))))
contrasts(d_sim_us_th$urban_rural_cat2) <- cbind("_rural" = c(-1, 1))
contrasts(d_sim_us_th$education_cat2) <- cbind("_coll" = c(-1, 1))
contrasts(d_sim_us_th$ethnicity_cat2) <- cbind("_nonThai" = c(-1, 1))
contrasts(d_sim_us_th$religion_cat3) <- cbind("_christian" = c(-1, 1, 0),
                                                  "_other" = c(-1, 0, 1))
```

### Age

```{r}
d_sim_us_th %>%
  ggplot(aes(x = age, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_smooth(method = "loess", span = 1)
```
```{r}
lm(scale(MSE) ~ scale(age), data = d_sim_us_th) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age), data = d_sim_us_th) %>% summary()
```


### Gender

```{r}
d_sim_us_th %>%
  ggplot(aes(x = gender, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(gender) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(gender) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ gender, data = d_sim_us_th) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ gender, data = d_sim_us_th) %>% summary()
```

### Race/ethnicity

```{r}
d_sim_us_th %>%
  ggplot(aes(x = ethnicity_cat, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(ethnicity_cat) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(ethnicity_cat) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
# lm(scale(MSE) ~ ethnicity_cat2, data = d_sim_us_th) %>% summary() # all thai
```

```{r}
# betareg(MSE_rescaled ~ ethnicity_cat2, data = d_sim_us_th) %>% summary() # all thai
```

### Education

```{r}
d_sim_us_th %>%
  ggplot(aes(x = education_catX, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(education_catX) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(education_catX) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ scale(education_catX), 
   data = d_sim_us_th %>% mutate(education_catX = as.numeric(education_catX))) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ scale(education_catX), 
        data = d_sim_us_th %>% mutate(education_catX = as.numeric(education_catX))) %>% summary()
```

```{r}
d_sim_us_th %>%
  ggplot(aes(x = education_cat2, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(education_cat2) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(education_cat2) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ education_cat2, data = d_sim_us_th) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ education_cat2, data = d_sim_us_th) %>% summary()
```

### Rural/urban

```{r}
d_sim_us_th %>%
  ggplot(aes(x = urban_rural_cat2, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(urban_rural_cat2) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(urban_rural_cat2) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ urban_rural_cat2, data = d_sim_us_th) %>% summary()
```


```{r}
betareg(MSE_rescaled ~ urban_rural_cat2, data = d_sim_us_th) %>% summary()
```

### Religion

```{r}
d_sim_us_th %>%
  ggplot(aes(x = religion_cat3, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(religion_cat3) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(religion_cat3) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
# lm(scale(MSE) ~ religion_cat3, data = d_sim_us_th) %>% summary() # almost all buddhist
```

```{r}
# betareg(MSE_rescaled ~ religion_cat3, data = d_sim_us_th) %>% summary() # almost all buddhist
```

### Target

```{r}
d_sim_us_th %>%
  mutate(target = factor(target, levels = levels_target_univ)) %>%
  ggplot(aes(x = target, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(target) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(target) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean), 
                  position = position_dodge(width = 0.25))
```

```{r}
lm(scale(MSE) ~ target, data = d_sim_us_th, contrasts = list(target = "contr.sum")) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ target, data = d_sim_us_th) %>% summary()
```

### All together

```{r}
lm(scale(MSE) ~ scale(age) + gender + 
     scale(education_catX) + 
     # ethnicity_cat2 + religion_cat3 + almost all thai and buddhist 
     urban_rural_cat2 + 
     target, 
   data = d_sim_us_th %>%
     mutate(education_catX = as.numeric(education_catX))) %>% 
  summary()
```

```{r}
lm(scale(MSE) ~ scale(age) + gender + 
     education_cat2 + 
     # ethnicity_cat2 + religion_cat3 + almost all thai and buddhist 
     urban_rural_cat2 + 
     target, 
   data = d_sim_us_th) %>% 
  summary()
```

```{r}
lmer(scale(MSE) ~ scale(age) + gender + 
       scale(education_catX) + 
     # ethnicity_cat2 + religion_cat3 + almost all thai and buddhist 
       urban_rural_cat2 + 
       + (1 | target), 
     data = d_sim_us_th %>%
       mutate(education_catX = as.numeric(education_catX))) %>% 
  summary()
```

```{r}
lmer(scale(MSE) ~ scale(age) + gender + 
       education_cat2 + 
     # ethnicity_cat2 + religion_cat3 + almost all thai and buddhist 
       urban_rural_cat2 + 
       + (1 | target), 
     data = d_sim_us_th) %>% 
  summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age) + gender + 
          scale(education_catX) + 
     # ethnicity_cat2 + religion_cat3 + almost all thai and buddhist 
          urban_rural_cat2 + 
          target, 
        data = d_sim_us_th %>%
          mutate(education_catX = as.numeric(education_catX))) %>% 
  summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age) + gender + 
     education_cat2 + 
       # ethnicity_cat2 + religion_cat3 + almost all thai and buddhist 
       urban_rural_cat2 + 
     target, 
   data = d_sim_us_th %>%
     mutate(education_catX = as.numeric(education_catX))) %>% 
  summary()
```

## China

```{r}
contrasts(d_sim_us_ch$gender) <- cbind("_m" = c(-1, 1))
contrasts(d_sim_us_ch$target) <- contr.sum(length(levels(factor(d_sim_us_ch$target))))
contrasts(d_sim_us_ch$urban_rural_cat2) <- cbind("_rural" = c(-1, 1))
contrasts(d_sim_us_ch$education_cat2) <- cbind("_coll" = c(-1, 1))
contrasts(d_sim_us_ch$ethnicity_cat2) <- cbind("_nonHan" = c(-1, 1))
contrasts(d_sim_us_ch$religion_cat3) <- cbind("_buddhist" = c(-1, 1, 0),
                                                  "_other" = c(-1, 0, 1))
```

### Age

```{r}
d_sim_us_ch %>%
  ggplot(aes(x = age, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_smooth(method = "loess", span = 1)
```
```{r}
lm(scale(MSE) ~ scale(age), data = d_sim_us_ch) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age), data = d_sim_us_ch) %>% summary()
```


### Gender

```{r}
d_sim_us_ch %>%
  ggplot(aes(x = gender, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(gender) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(gender) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ gender, data = d_sim_us_ch) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ gender, data = d_sim_us_ch) %>% summary()
```

### Race/ethnicity

```{r}
d_sim_us_ch %>%
  ggplot(aes(x = ethnicity_cat, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(ethnicity_cat) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(ethnicity_cat) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
d_sim_us_ch %>%
  ggplot(aes(x = ethnicity_cat2, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(ethnicity_cat2) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(ethnicity_cat2) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
# lm(scale(MSE) ~ ethnicity_cat2, data = d_sim_us_ch) %>% summary() # almost all han
```

```{r}
# betareg(MSE_rescaled ~ ethnicity_cat2, data = d_sim_us_ch) %>% summary() # almost all han
```

### Education

```{r}
d_sim_us_ch %>%
  ggplot(aes(x = education_catX, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(education_catX) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(education_catX) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ scale(education_catX), 
   data = d_sim_us_ch %>% mutate(education_catX = as.numeric(education_catX))) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ scale(education_catX), 
        data = d_sim_us_ch %>% mutate(education_catX = as.numeric(education_catX))) %>% summary()
```

```{r}
d_sim_us_ch %>%
  ggplot(aes(x = education_cat2, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(education_cat2) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(education_cat2) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ education_cat2, data = d_sim_us_ch) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ education_cat2, data = d_sim_us_ch) %>% summary()
```

### Rural/urban

```{r}
d_sim_us_ch %>%
  ggplot(aes(x = urban_rural_cat2, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(urban_rural_cat2) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(urban_rural_cat2) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ urban_rural_cat2, data = d_sim_us_ch) %>% summary()
```


```{r}
betareg(MSE_rescaled ~ urban_rural_cat2, data = d_sim_us_ch) %>% summary()
```

### Religion

```{r}
d_sim_us_ch %>%
  ggplot(aes(x = religion_cat3, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(religion_cat3) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(religion_cat3) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ religion_cat3, data = d_sim_us_ch) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ religion_cat3, data = d_sim_us_ch) %>% summary()
```

### Target

```{r}
d_sim_us_ch %>%
  mutate(target = factor(target, levels = levels_target_univ)) %>%
  ggplot(aes(x = target, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(target) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(target) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean), 
                  position = position_dodge(width = 0.25))
```

```{r}
lm(scale(MSE) ~ target, data = d_sim_us_ch, contrasts = list(target = "contr.sum")) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ target, data = d_sim_us_ch) %>% summary()
```

### All together

```{r}
lm(scale(MSE) ~ scale(age) + gender + 
     scale(education_catX) + 
     # ethnicity_cat2 + # almost all han
     religion_cat3 + urban_rural_cat2 + 
     target, 
   data = d_sim_us_ch %>%
     mutate(education_catX = as.numeric(education_catX))) %>% 
  summary()
```

```{r}
lm(scale(MSE) ~ scale(age) + gender + 
     education_cat2 + 
     # ethnicity_cat2 + # almost all han
     religion_cat3 + urban_rural_cat2 + 
     target, 
   data = d_sim_us_ch) %>% 
  summary()
```

```{r}
lmer(scale(MSE) ~ scale(age) + gender + 
       scale(education_catX) + 
     # ethnicity_cat2 + # almost all han
     religion_cat3 + urban_rural_cat2 + 
       + (1 | target), 
     data = d_sim_us_ch %>%
       mutate(education_catX = as.numeric(education_catX))) %>% 
  summary()
```

```{r}
lmer(scale(MSE) ~ scale(age) + gender + 
       education_cat2 + 
     # ethnicity_cat2 + # almost all han
     religion_cat3 + urban_rural_cat2 + 
       + (1 | target), 
     data = d_sim_us_ch) %>% 
  summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age) + gender + 
          scale(education_catX) + 
     # ethnicity_cat2 + # almost all han
     religion_cat3 + urban_rural_cat2 + 
          target, 
        data = d_sim_us_ch %>%
          mutate(education_catX = as.numeric(education_catX))) %>% 
  summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age) + gender + 
     education_cat2 + 
     # ethnicity_cat2 + # almost all han
     religion_cat3 + urban_rural_cat2 + 
     target, 
   data = d_sim_us_ch %>%
     mutate(education_catX = as.numeric(education_catX))) %>% 
  summary()
```

## Vanuatu

```{r}
contrasts(d_sim_us_vt$gender) <- cbind("_m" = c(-1, 1))
contrasts(d_sim_us_vt$target) <- contr.sum(length(levels(factor(d_sim_us_vt$target))))
contrasts(d_sim_us_vt$location_cat2) <- cbind("_urban" = c(-1, 1))
```

### Age

```{r}
d_sim_us_vt %>%
  ggplot(aes(x = age, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_smooth(method = "loess", span = 1)
```
```{r}
lm(scale(MSE) ~ scale(age), data = d_sim_us_vt) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age), data = d_sim_us_vt) %>% summary()
```


### Gender

```{r}
d_sim_us_vt %>%
  ggplot(aes(x = gender, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(gender) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(gender) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ gender, data = d_sim_us_vt) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ gender, data = d_sim_us_vt) %>% summary()
```

### Location

```{r}
d_sim_us_vt %>%
  ggplot(aes(x = location_cat2, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(location_cat2) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(location_cat2) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean))
```

```{r}
lm(scale(MSE) ~ location_cat2, data = d_sim_us_vt) %>% summary()
```


```{r}
betareg(MSE_rescaled ~ location_cat2, data = d_sim_us_vt) %>% summary()
```

### Target

```{r}
d_sim_us_vt %>%
  mutate(target = factor(target, levels = levels_target_univ)) %>%
  ggplot(aes(x = target, y = MSE)) +
  geom_jitter(height = 0, alpha = 0.2) +
  geom_text(data = . %>% count(target) %>% data.frame(),
            aes(y = 0.5, label = paste("n =", n))) +
  geom_pointrange(data = . %>% 
                    group_by(target) %>%
                    multi_boot_standard(col = "MSE", na.rm = T),
                  aes(ymin = ci_lower, ymax = ci_upper, y = mean), 
                  position = position_dodge(width = 0.25))
```

```{r}
lm(scale(MSE) ~ target, data = d_sim_us_vt, contrasts = list(target = "contr.sum")) %>% summary()
```

```{r}
betareg(MSE_rescaled ~ target, data = d_sim_us_vt) %>% summary()
```

### All together


```{r}
lm(scale(MSE) ~ scale(age) + gender + 
     location_cat2 + 
     target, 
   data = d_sim_us_vt) %>% 
  summary()
```

```{r}
lmer(scale(MSE) ~ scale(age) + gender + 
     location_cat2 + 
       + (1 | target), 
     data = d_sim_us_vt) %>% 
  summary()
```

```{r}
betareg(MSE_rescaled ~ scale(age) + gender + 
     location_cat2 + 
          target, 
        data = d_sim_us_vt) %>% 
  summary()
```


# Summary stats

## Group-level average matching scores (observed)

```{r}
bind_rows(mod_cult_fun(d_us_adults_w) %>% mutate(country = "US"),
          mod_cult_fun(d_gh_adults_w) %>% mutate(country = "Ghana"),
          mod_cult_fun(d_th_adults_w) %>% mutate(country = "Thailand"),
          mod_cult_fun(d_ch_adults_w) %>% mutate(country = "China"),
          mod_cult_fun(d_vt_adults_w) %>% mutate(country = "Vanuatu")) %>%
  mutate(country = factor(country, levels = levels_country)) %>%
  group_by(country) %>%
  summarise(min = min(mean_match), max = max(mean_match))
```

## Theoretical range for MSE 

### Kinda = 0.5

```{r}
bind_rows(mse_minmax_fun(mod_cult_fun(d_us_adults_w)) %>% mutate(country = "US"),
          mse_minmax_fun(mod_cult_fun(d_gh_adults_w)) %>% mutate(country = "Ghana"),
          mse_minmax_fun(mod_cult_fun(d_th_adults_w)) %>% mutate(country = "Thailand"),
          mse_minmax_fun(mod_cult_fun(d_ch_adults_w)) %>% mutate(country = "China"),
          mse_minmax_fun(mod_cult_fun(d_vt_adults_w)) %>% mutate(country = "Vanuatu")) %>%
  mutate(country = factor(country, levels = levels_country)) %>%
  mutate_if(is.numeric, ~format(round(., 2), nsmall = 2)) 
```

### Kinda = yes = 1

```{r}
bind_rows(mse_minmax_fun(mod_cult_fun(d_us_adults_w, kinda = 0.5)) %>% mutate(country = "US"),
          mse_minmax_fun(mod_cult_fun(d_gh_adults_w, kinda = 0.5)) %>% mutate(country = "Ghana"),
          mse_minmax_fun(mod_cult_fun(d_th_adults_w, kinda = 0.5)) %>% mutate(country = "Thailand"),
          mse_minmax_fun(mod_cult_fun(d_ch_adults_w, kinda = 0.5)) %>% mutate(country = "China"),
          mse_minmax_fun(mod_cult_fun(d_vt_adults_w, kinda = 0.5)) %>% mutate(country = "Vanuatu")) %>%
  mutate(country = factor(country, levels = levels_country)) %>%
  mutate_if(is.numeric, ~format(round(., 2), nsmall = 2))
```

## MSE summary stats by group (observed) 

```{r}
d_sim_us_adults %>%
  full_join(d_sim_gh_adults) %>%
  full_join(d_sim_th_adults) %>%
  full_join(d_sim_ch_adults) %>%
  full_join(d_sim_vt_adults) %>%
  full_join(d_sim_us_children) %>% select(-starts_with("sib")) %>%
  full_join(d_sim_gh_children) %>% select(-starts_with("sib")) %>%
  full_join(d_sim_th_children) %>% select(-starts_with("sib")) %>%
  full_join(d_sim_ch_children) %>% select(-starts_with("sib")) %>%
  full_join(d_sim_vt_children) %>% select(-starts_with("sib")) %>%
  mutate(age_group = case_when(grepl("adults", subj_id) ~ "adults",
                               grepl("children", subj_id) ~ "children")) %>%
  mutate(country = factor(country, levels = levels_country)) %>%
  group_by(country, age_group) %>%
  mutate(dev_rescale = scales::rescale(MSE)) %>%
  summarise(min_MSE = min(MSE, na.rm = T),
            max_MSE = max(MSE, na.rm = T),
            mean_MSE  = mean(MSE, na.rm = T),
            sd_MSE = sd(MSE, na.rm = T),
            median_MSE = median(MSE, na.rm = T),
            min_MSE_rescaled = min(MSE_rescaled, na.rm = T),
            max_MSE_rescaled = max(MSE_rescaled, na.rm = T),
            mean_MSE_rescaled  = mean(MSE_rescaled, na.rm = T),
            sd_MSE_rescaled = sd(MSE_rescaled, na.rm = T),
            median_MSE_rescaled = median(MSE_rescaled, na.rm = T)) %>%
  arrange(age_group, country) %>%
  mutate_if(is.numeric, ~format(round(., 2), nsmall = 2))
```

